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Compressed Baryonic Matter Experiment 

Technical Status Report 

CBM experiment 

January 2005 

CBM collaboration

ii 

Sketch of the planned Compressed Baryonic Matter (CBM) experiment with the HADES spectrometer 

positioned in front of it. The CBM setup consists of a superconducting dipole magnet with a Silicon 

tracker System inside, a Rich Imaging Cherenkov detector (RICH) for electron identification, the three 

Transition Radiation Detectors (TRD), the Time-Of Flight (TOF) wall which is a Resistive Plate Chamber 

(RPC), and the electromagnetic calorimeter (ECAL). The total length of the setup is approximately 12 m 

from target at the entrance of the dipole to the ECAL.

The CBM Collaboration 

• Bergen, Norway, Department of Physics and Technology, University of Bergen 

D. Röhrich, K. Ullaland 

• Bucharest, Romania, National Institute for Physics and Nuclear Engineering 

C. Aiftimiei, V. Catanescu, D. Moisa, M. Petris, A. Petrovici, M. Petrovici, A. Raduta, G. Stoicea 

• Budapest, Hungary, Eötvös University 

F. Deak, R. Izsak, A. Kiss 

• Budapest, Hungary, KFKI 

E. Denes, Z. Fodor, J. Kecskemeti, Cs. Soos, T. Kiss, G. Vesztergombi 

• Coimbra, Portugal, LIP 

P. Fonte, R. Ferreira Marques, A. Policarpo 

• Darmstadt, Germany, GSI 

M. Al-Turany, A. Andronic, E. Badura, E. Berdermann, D. Bertini, P. Braun-Munzinger, M. Ciobanu, 

H. Deppe, M. Deveaux, J. Eschke, H. Essel, H. Flemming, V. Friese, T. Galatyuk, C. Garabatos, 

C. Höhne, R. Holzmann, E. Jimenes, M. Kalisky, A. Kiseleva, K. Koch, P. Koczon, W. König, 

B. Kolb, G. Kružić, Y. Leifels, S. Linev, C. Lippmann, W.F.J. Müller, W. Niebur, A. Schüttauf, 

K. Schwarz, P. Senger, R. Simon, F. Uhlig, I. Vassiliev 

• Dubna, Russia, JINR-LHE 

V. Chepurnov, S. Chernenko, O. Fateev, V. Golovatyuk, A. Ierusalimov, V. Ladygin, A. Malakhov, 

E. Matyushevsky, E. Plekhanov, V. Pozdniakov, O. Rogachevsky, Yu. Zanevsky, V. Zrjuev 

• Dubna, Russia, JINR-LPP 

Ju. Gousakov, N. Grigalashvili, G. Kekelidze, V. Lucenko, S. Mishin, D. Peshekhonov, V. Peshekhonov, 

O. Strekalovsky, K. Viriasov, J. Zlobin 

• Dubna, Russia, JINR-LIT 

P. Akishin, E. Akishina, S. Baginyan, Victor Ivanov, Valery Ivanov, B. Kostenko, E. Litvinenko, 

G. Ososkov, A. Raportirenko, A. Soloviev, P. Zrelov, V. Uzhinsky 

• Dubna, Russia, JINR-LTP 1 

D. Blaschke, V. Burov, V. Toneev 

• Frankfurt, Germany, Institut für Kernphysik, Universität Frankfurt 

H. Appelshäuser, C. Müntz, H. Ströbele, J. Stroth 

• Heidelberg, Germany, 2. Physikalisches Institut, Universität Heidelberg 

M.L. Benabderahmane, E. Cordier, N. Herrmann, A. Mangiarotti 

• Heidelberg, Germany, Kirchhoff-Institut für Physik, Universität Heidelberg 

D. Atanasov, U. Kebschull (Universität Leipzig), I. Kisel, V. Lindenstruth, G. Torralba, G. Tröger 

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• Kaiserslautern, Deutschland, Universität Kaiserslautern 1 

D. Muthers, R. Tielert, S. Tontisirin 

• Katowice, Poland, University of Silesia 

A. Bubak, A. Grzeszczuk, S. Kowalski, M. Krauze, E. Stephan, W. Zipper 

• Krakow, Poland, Jagiellonian University 

J. Brzychczyk, R. Karabowicz, Z. Majka, P. Staszel, P. Szostak 

• Kyiv, Ukraine, National University of Kyiv 

O. Bezshyyko, I. Kadenko, D. Kresan, V. Plujko, V. Shevshenko 

• Mannheim, Germany, Inst. of Computer Engineering, Universität Mannheim 

K.-H. Brenner, U. Brüning, P. Fischer, J. Gläß, P. Haspel, R. Männer, D. Slogsnat, C. Steinle, 

D. Wohlfeld, A. Wurz 

• Marburg, Germany, Fachbereich Physik, Universität Marburg 

B. Kohlmeyer, C. Schindler 

• Moscow, Russia, Institute for Nuclear Research 

M. Golubeva, F. Guber, A. Ivashkin, O. Karavichev, T. Karavicheva, E. Karpechev, A. Kurepin, 

A. Maevskaia, I. Peshenichnov, V. Rasin, A. Reshetin, V. Tiflov, N. Topilskaya 

• Moscow, Russia, ITEP 

A. Akindinov, A. Arefiev, Y. Grishuk, A. Golutvin, S. Kiselev, I. Korolko, T. Kvaracheliya, V. Maiatski 

, S. Malyshev, A. Martemiyanov, K. Mikhailov, P. Polozov, M. Prokudin, A. Smirnitskiy, 

A. Stavinsky, V. Stolin, K. Voloshin, B. Zagreev 

Smolyankin laboratory: Y. Grishkin, A. Lebedev, N. Rabin, V. Smolyankin, A. Zhilin 

• Moscow, Russia, SINP, Moscow State University 

N. Baranova, G. Bashindjagyan, D. Karmanov, M. Korolev, M. Merkin, A. Voronin 

• Moscow, Russia, Kurchatov Institute 

A. Kazantsev, V. Manko, I. Yushmanov 

• Moscow, Russia, MEPhi 1 

M. Alyushin, E. Atkin, Y. Bocharov, B. Bogdanovich, V. Emelianov, I. Ilyushenko, A. Krasnuk, 

E. Onishchenko, V. Popov, A. Silaev, A. Simakov, Y. Volkov 

• Münster, Germany, Institut für Kernphysik, Universität Münster 

D. Bucher, M. Hoppe, K. Reygers, A. Wilk, J. Wessels 

• Nikosia, Cyprus, Cyprus University 

J. Mousa, H. Tsertos 

• Obninsk, Russia, Obninsk State University of Atomic Energy 

V. Galkin, A. Golovin, D. Ossetski, D. Ryzhikov, V. Saveliev, M. Zaboudko 

• Prag, Czech Republic, Technical University 

V. Petracek, L. Skoda

• Protvino, Russia, IHEP 

V. Ammosov, M. Bogolyubsky, V. Dyatchenko, Yu. Kharlov, V. Khmelnikov, A. Kuznetsov, V. Leontiev, 

V. Obraztsov, B. Polishchuk, V. Rykalin, A. Ryazantsev, S. Sadovsky, A. Semak, V. Shelikhov, 

A. Soldatov, V. Sougonyaev, Y. Sviridov, V. Victorov, V. Zaets 

• Pusan, Korea, Pusan National University 

J.K. Ahn, D.-S. Kim, J.-Y. Kim, I.-K. Yoo 

• Rez, Czech Republic, Czech Academy of Sciences 

D. Adamova, A. Kugler, J. Novotny, P. Tlusty 

• Rossendorf, Germany, FZR, Institut für Kern- und Hadronenphysik 

F. Dohrmann, E. Grosse, K. Heidel, B. Kämpfer, R. Kotte, L. Naumann 

• Santiago de la Compostela, Spain, University 

M. Angeles Lopez, D. Belver, J. Garzon, F. Gomez, D. Gonzalez 

• Seoul, Korea, Korea University 

B. Hong, Y.J. Kim, K.S. Sim 

• St. Petersburg, Russia, Khlopin Radium Institute (KRI) 

M. Chubarov, V. Karasev, S. Loshaev, Y. Murin, V. Plujschev, E. Seleznev 

• St. Petersburg, Russia, CKBM 

A. Bolonin, V. Dobulevich, S. Igolkin, G. Karasev, G. Petrova, A. Svischev, M. Tkachev, V. Varava, 

A. Vasiliev, A. Vorobeva, V. Yaritzin 

• St. Petersburg, Russia, PNPI 

V. Ivanov, A. Khanzadeev, A. Nadtochii, Y. Riabov, V. Samsonov, D. Seliverstov, M. Zhalov 

• St. Petersburg, Russia, St. Petersburg State Polytechnic University 

Y. Berdnikov, E. Kryshen, M. Ryzhinskiy, A. Tsvetkov 

• Strasbourg, France, IN2P3-CNRS/ULP (IRes) 

A. Besson, G. Gaycken, S. Heini, F. Rami, H. Souffi-Kebbati, M. Winter 

• Warsaw, Poland, Warsaw University, Institute of Experimental Physics 

M. Kirejczyk, T. Matulewicz, B. Sikora, K. Siwek-Wilczynska, L. Slusarczyk, K. Wisniewski 

• Wuhan, China, Institute of Particle Physics, Hua-zhong Normal University 1 

X. Cai, T. Wu, Z.B. Yin, D.C. Zhou 

• Zagreb, Croatia, Rudjer Bošković Institute 

Z. Basrak, R. Čaplar, M. Dželalija, I. Gašparić, M. Kiš 

1 membership to be confirmed by the collaboration board 

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Persons additionally contributing to this report: 

E.L. Bratkovskaya, Institut für Theoretische Physik Frankfurt, Germany 

B. Friman, A. Martemiyanov, and P. Moritz GSI Darmstadt, Germany 

S. Gorbunov, DESY Zeuthen, Germany 

J. Marzec, K. Zaremba, Warsaw University of Technology, Poland 

H.K. Soltveit, Physikalisches Institut, Universität Heidelberg, Germany 

V. Tikhomirov, Lebedev Physical Inst., Moscow, Russia 

P. Wintz, Forschungszentrum Jülich, Germany 

A. Bertin, M. Bruschi, B. Giacobbe, N. Semprini-Cesari, R. Spighi, M. Villa, A. Vitale, A. Zoccoli, 

Instituto Nazionale di Fisica Nucleare - Sezione di Bologna, Italy 

Acknowledgement 

This work is supported by the EU Integrated Infrastructure Initiative Hadron-Physics Project under Contract 

number RII3-CT-2004-506078 and by INTAS (contract numbers 03-51-6645, 03-54-6272, 03-54- 

4169, 03-54-5119, and 03-54-3891).

Contents 

The CBM Collaboration iii 

Preface 1 

I Introduction and Overview 3 

1 Introduction and Overview 5 

1.1 Physics case and observables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 

1.1.1 Low-mass vector mesons . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 

1.1.2 Charm production . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8 

1.1.3 Strangeness in dense matter . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 

1.1.4 Event-by-event fluctuations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 

1.2 The CBM experiment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12 

1.2.1 The research program . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12 

1.2.2 The detector system . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14 

1.2.3 Detector performance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18 

1.2.4 Planning and organization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21 

II Detector Systems 23 

2 The Silicon Tracking Station (STS) 25 

2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25 

2.1.1 Tracking in CBM . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25 

2.1.2 Operational environment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26 

2.1.3 Overall geometry . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26 

2.1.4 Status of R&D and design optimization . . . . . . . . . . . . . . . . . . . . . . . . . 27 

2.2 The inner tracking station (ITS) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27 

2.2.1 Design aspects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27 

2.2.2 Conceptional design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29 

2.2.3 Monolithic Active Pixel Sensors (MAPS) . . . . . . . . . . . . . . . . . . . . . . . . 29 

2.2.4 Alternative technologies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39 

2.3 The Silicon Strip Tracker (SST) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44 

2.3.1 General design considerations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44 

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2.3.2 Geometry of the Si-strip tracker . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47 

2.3.3 Si-strip sensors: Design and Technology . . . . . . . . . . . . . . . . . . . . . . . . 52 

2.3.4 Si-strip STS Readout Electronics . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53 

2.3.5 Working packages, timelines, cost estimate . . . . . . . . . . . . . . . . . . . . . . 57 

3 Ring Imaging Cherenkov detector (RICH) 59 

3.1 Design considerations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59 

3.2 Particle identification with the RICH detector . . . . . . . . . . . . . . . . . . . . . . . . 61 

3.3 RICH simulations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65 

3.3.1 Description of simulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65 

3.3.2 Response of the RICH detector to single particles . . . . . . . . . . . . . . . . . . . 66 

3.3.3 Acceptance of the RICH detector . . . . . . . . . . . . . . . . . . . . . . . . . . . . 68 

3.3.4 Response of the RICH detector to heavy-ion collisions . . . . . . . . . . . . . . . . . 69 

3.4 Technical realization of the RICH detector . . . . . . . . . . . . . . . . . . . . . . . . . 72 

3.4.1 Optics and mirrors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 72 

3.4.2 Radiator . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 74 

3.4.3 UV photodetector . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 76 

3.4.4 HV and readout electronics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 80 

3.4.5 Support structure, gas vessel and gas supply system . . . . . . . . . . . . . . . . . . 81 

3.5 Working packages, timelines, costs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 83 

4 Transition Radiation detector (TRD) 85 


4.2 An MWPC-based TRD . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 85 

4.2.1 Simulations of electron/pion identification . . . . . . . . . . . . . . . . . . . . . . . 86 

4.2.2 Tests with prototypes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 91 

4.2.3 Gas system . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 98 

4.2.4 Readout electronics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 100 

4.2.5 Design considerations, mechanics, and material budget . . . . . . . . . . . . . . . . 102 

4.2.6 Costs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 103 

4.3 A straw-based TRD . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 103 

4.3.1 Monte Carlo simulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 104 

4.3.2 Design of a TRT for CBM . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 108 

4.3.3 Detector elements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 111 

4.3.4 Gas supply system . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 114 

4.3.5 Detector readout . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 116

4.3.6 Costs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 118 

4.4 Detector ageing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 118 

4.5 Infrastructure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 118 

4.6 Working packages and timelines . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 120 

5 Resistive Plate Chambers (RPC) 121 


5.2 Simulations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 122 

5.2.1 Input data and parameters of simulation . . . . . . . . . . . . . . . . . . . . . . . . . 122 

5.2.2 Matching of tracks with TOF . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 122 

5.3 RPC properties . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 124 

5.3.1 Rate capability . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 124 

5.3.2 Aging . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 132 

5.4 RPC detector layout options . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 136 

5.4.1 Single cell chambers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 136 

5.4.2 Multipad chambers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 136 

5.4.3 Single strip counter . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 141 

5.4.4 Multistrip counter . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 144 

5.5 RPC electronics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 149 

5.5.1 FOPI-RPC-Electronics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 149 

5.5.2 Preamplifier/discriminator . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 150 

5.5.3 Time Measurement and Digital Backend . . . . . . . . . . . . . . . . . . . . . . . . 152 

5.6 TOF detector test facility . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 156 

5.7 Infrastructure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 158 

5.8 Working packages and milestones . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 159 

5.9 Cost estimate . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 159 

6 Electromagnetic Calorimeter (ECAL) 161 


6.2 Simulations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 162 

6.2.1 Acceptance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 165 

6.2.2 e/π separation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 165 

6.3 Technology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 168 

6.4 Detector construction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 170 

6.4.1 Module design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 170 

6.5 Photon detectors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 172 

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6.6 Readout electronics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 173 

6.7 Radiation hardness of detectors and electronics . . . . . . . . . . . . . . . . . . . . . . . 173 

6.8 Working packages, timelines, costs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 174 

7 Superconducting dipole magnet 177 

7.1 Dipole with parallel pole shoes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 177 

7.2 Dipole with inclined pole shoes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 178 

8 Diamond start detector 183 

8.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 183 

8.2 Properties of CVD diamond detectors . . . . . . . . . . . . . . . . . . . . . . . . . . . . 184 

8.2.1 Pulse shape characteristics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 184 

8.2.2 Intrinsic time resolution of HI diamond detectors at SIS energies . . . . . . . . . . . 185 

8.2.3 MIP timing with PC diamond detectors . . . . . . . . . . . . . . . . . . . . . . . . . 185 

8.2.4 Suppression of trigger rate and background using START-VETO devices . . . . . . . 186 

8.2.5 Pulse-height distributions in CVD-diamond detectors . . . . . . . . . . . . . . . . . 187 

8.3 Discussion of the strip design with respect to the beam parameters . . . . . . . . . . . . . 188 

8.3.1 Beam dimensions and beam intensity load at different distance from the CBM target 

assuming 50 µm strip and readout pitch at a strip width of 12 µm . . . . . . . . . . . 188 

8.3.2 Time Zero precision . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 190 

8.4 Influence of diamond material type and thickness . . . . . . . . . . . . . . . . . . . . . . 191 

8.4.1 Available types of CVD-D material and their corresponding detector properties . . . . 191 

8.4.2 Multiple scattering and energy straggling . . . . . . . . . . . . . . . . . . . . . . . . 192 

8.5 FE Electronics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 192 

8.6 Summary, concluding remarks and outlook . . . . . . . . . . . . . . . . . . . . . . . . . 193 

9 Common Front-End Electronics Aspects 195 

9.1 General Considerations, Requirements . . . . . . . . . . . . . . . . . . . . . . . . . . . 195 

9.1.1 Considerations with respect to electronics . . . . . . . . . . . . . . . . . . . . . . . . 197 

9.1.2 Microelectronics technology choice . . . . . . . . . . . . . . . . . . . . . . . . . . . 198 

9.2 Existing microelectronics building blocks . . . . . . . . . . . . . . . . . . . . . . . . . . 200 

9.2.1 TRAP-ADC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 200 

9.2.2 LVDS IO Cells. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 201 

9.2.3 On-Chip Slow Control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 201 

9.2.4 Analogue multiplexer, auxiliary ADC, temperature sensor and voltage reference . . . 202 

9.2.5 Digital Filters . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 202 

9.2.6 High-speed Tracklet Processor . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 203

9.2.7 32-bit RISC CPU . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 203 

9.2.8 Network Interface . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 203 

9.2.9 High Performance ALU . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 204 

9.2.10 Quad Port Memory . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 204 

9.2.11 Programmable Delay Unit & Network Fault Tolerance . . . . . . . . . . . . . . . . . 204 

9.2.12 SCSN Redundant High-speed Configuration Network . . . . . . . . . . . . . . . . . 204 

9.3 Overall Architecture . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 205 

9.4 First Amplifier Stages . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 208 

9.5 Analog Memory . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 210 

9.6 Multi-purpose ADC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 211 

9.6.1 General requirements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 211 

9.6.2 Variant 1: Interleaving (Multiplexing) of 4 ADCs . . . . . . . . . . . . . . . . . . . . 211 

9.6.3 Variant 2: Pipeline-ADC with 10/12 bit . . . . . . . . . . . . . . . . . . . . . . . . . 212 

9.6.4 Variant 3: 9bit Pipeline-ADC with 2 different gain stages . . . . . . . . . . . . . . . 212 

9.7 SerDes, Clock recovery and Optical links . . . . . . . . . . . . . . . . . . . . . . . . . . 213 

9.7.1 High Performance Clock Recovery . . . . . . . . . . . . . . . . . . . . . . . . . . . 213 

9.7.2 SerDes and Optical driver . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 215 

9.7.3 Low cost electrical / optical conversion . . . . . . . . . . . . . . . . . . . . . . . . . 220 

9.8 FEE Communication . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 221 

9.8.1 Clock/Time Distribution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 221 

9.8.2 Generic Optical Advanced Serializer/Deserializer (OASE) . . . . . . . . . . . . . . . 222 

9.9 CNet – Concentrator Network . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 225 

9.9.1 General Requirements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 225 

9.9.2 CNet System Level . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 226 

9.9.3 Link Protocol Physical Layer (FELA) . . . . . . . . . . . . . . . . . . . . . . . . . . 228 

9.10 Miscellaneous, Infrastructure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 232 

9.10.1 Power distribution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 232 

9.10.2 FPGA Roadmap . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 233 

9.10.3 Radiation Tolerance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 234 

10 Data Acquisition, Event Selection, Controls, On-line/Off-line Computing 237 

10.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 237 

10.2 Overall Architecture . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 238 

10.3 Communication and Processing Architecture . . . . . . . . . . . . . . . . . . . . . . . . 241 

10.3.1 BNet - Build Strategies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 241 

10.3.2 First Level Farm Strategies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 249 

xi

xii 

10.3.3 Implementation Plan for First Level Farm . . . . . . . . . . . . . . . . . . . . . . . . 255 

10.4 Feature Extraction and Event Selection . . . . . . . . . . . . . . . . . . . . . . . . . . . 258 

10.4.1 Filter Strategy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 259 

10.4.2 Implementation Scenarios for Selected Algorithms . . . . . . . . . . . . . . . . . . . 260 

10.5 Controls (ECS) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 264 

10.5.1 General Description . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 265 

10.5.2 Detailed requirements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 265 

10.5.3 Possible Implementation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 266 

10.5.4 The Detector Control System (DCS) . . . . . . . . . . . . . . . . . . . . . . . . . . 267 

10.5.5 Configurations and Update Management . . . . . . . . . . . . . . . . . . . . . . . . 270 

10.6 Computing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 271 

10.6.1 Physics requirements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 271 

10.6.2 Event structure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 272 

10.6.3 High level event reconstruction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 273 

10.6.4 Estimate of computing power and storage . . . . . . . . . . . . . . . . . . . . . . . . 274 

10.6.5 Offline computing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 275 

10.6.6 Cost estimate . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 275 

10.7 CBM Simulation and Analysis Framework . . . . . . . . . . . . . . . . . . . . . . . . . 275 

10.7.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 275 

10.7.2 Design and implementation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 275 

11 Beam requirements and run time estimate 279 

11.1 Beam requirements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 279 

11.2 Runtime estimate . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 279 

11.2.1 Nucleus-nucleus collisions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 280 

11.2.2 Proton-nucleus and proton-proton collisions . . . . . . . . . . . . . . . . . . . . . . 282 

III Physics performance 285 

12 Experimental conditions 287 

12.1 Hit densities and rates . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 287 

12.2 Hadron multiplicities and momentum distributions from UrQMD calculations . . . . . . 295 

12.3 Hadron multiplicities from HSD calculations . . . . . . . . . . . . . . . . . . . . . . . . 297 

12.4 Hadron multiplicities: experimental results . . . . . . . . . . . . . . . . . . . . . . . . . 299 

12.5 Knock-On electrons . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 299

13 Event reconstruction 301 

13.1 Track finding . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 302 

13.1.1 Hough Transform . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 302 

13.1.2 Cellular Automaton method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 305 

13.1.3 Conformal Mapping . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 308 

13.1.4 3D track-following method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 308 

13.2 Track fitting . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 311 

13.2.1 Kalman filter – first approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 311 

13.2.2 Kalman filter - second approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 314 

13.2.3 Parabolic approximation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 315 

13.2.4 Polynomial approximation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 318 

13.2.5 Orthogonal polynomial sets . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 320 

13.3 Primary vertex fitting . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 323 

13.3.1 Minimisation of track impact parameters . . . . . . . . . . . . . . . . . . . . . . . . 323 

13.3.2 Geometrical fit with Kalman filter . . . . . . . . . . . . . . . . . . . . . . . . . . . . 323 

13.4 Secondary vertex fitting . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 326 

13.4.1 Geometrical fit with Kalman filter . . . . . . . . . . . . . . . . . . . . . . . . . . . . 326 

13.4.2 Mass and topological constrained fit with Kalman filter . . . . . . . . . . . . . . . . 327 

13.5 RICH ring finding . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 330 

13.5.1 Track extrapolation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 330 

13.5.2 Hough Transform . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 330 

13.5.3 Elastic Net for standalone RICH ring finding . . . . . . . . . . . . . . . . . . . . . . 333 

13.6 RICH ring fitting . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 337 

13.6.1 Robust ring fitter . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 337 

14 Hadron identification 339 

14.1 Acceptance for TOF identified particles . . . . . . . . . . . . . . . . . . . . . . . . . . . 339 

14.2 Hadron identification by time-of-flight . . . . . . . . . . . . . . . . . . . . . . . . . . . 339 

15 Hyperons 345 

15.1 Λ hyperons . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 345 

15.2 Ξ − hyperons . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 347 

15.3 Ω − hyperons . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 349 

15.4 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 350 

16 Low-mass vector mesons 353 

16.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 353 

xiii

xiv 

16.2 Simulation tools . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 353 

16.3 Background suppression - cut strategy . . . . . . . . . . . . . . . . . . . . . . . . . . . 354 

16.4 Results of signal to background ratio studies . . . . . . . . . . . . . . . . . . . . . . . . 354 

17 Charmonium 357 

17.1 J/ψ detection via e + e − decay . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 357 

17.1.1 Signal and background simulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . 357 

17.1.2 Acceptance and efficiency . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 359 

17.1.3 Signal to background ratio . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 360 

17.1.4 Online event selection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 361 

17.1.5 Fast track reconstruction in the TRD . . . . . . . . . . . . . . . . . . . . . . . . . . 362 

17.1.6 Conclusions and next steps . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 365 

17.2 J/ψ detection via µ + µ − decay . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 365 

17.2.1 Signal and background simulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . 365 

17.2.2 Signal to background ratio . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 366 

17.2.3 Identification of primary muons . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 369 

17.2.4 Conclusions and next steps . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 372 

18 Open charm 375 

18.1 Signal and background simulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 375 

18.2 Tracking and Vertexing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 375 

18.3 Background reduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 376 

18.4 Analysis and results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 378 

18.5 Influence of the material budget . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 380 

18.6 Next steps . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 380 

19 Event-by-event fluctuations 383 

IV Infrastructure and Safety 385 

20 Cave 387 

21 Radiation protection 389 

V Organization and Responsibilities, Planning 391 

22 Planning and organization 393

22.1 Cost estimates . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 393 

22.2 Responsibilities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 393 

22.3 Organization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 395 

22.4 Schedule . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 397 

References 401

xvi

Preface 

Since the submission of the Letter-of-Intent in January 2004 the CBM collaboration has concentrated on 

feasibility studies and detector R&D in order to reach the design goals formulated in the Conceptional 

Design Report of the FAIR project. This Technical Status Report documents the progress which has been 

made particularly in the following areas: 

• development and implementation of a common CBM simulation framework 

• design of the Ring Imaging Cherenkov detector 

• design of Transition Radiation Detectors 

• study of the high-rate performance of gas detectors and Resistive Plate Chambers, 

• improvement of read-out speed and radiation hardness of MAPS detectors 

• layout of the Silicon strip detectors 

• development of track reconstruction and pattern recognition algorithms 

• analysis techniques for low-mass dilepton pairs, charmonium, D-mesons and hyperons 

• feasibility studies on event-by-event fluctuations 

• concept studies of event selection, FEE and DAQ 

• design of a superconducting dipole magnet 

In spite of this progress more effort is needed in order to reach conclusion upon a detailed detector 

concept with optimized geometry and granularity. We have to demonstrate the feasibility to measure low 

mass dilepton pairs and D mesons by simulations which are based on track reconstruction in the magnetic 

field including realistic detector responses. Moreover, we have to prove that the detectors fulfill the 

requirements on rate capability, radiation hardness, spatial resolution, efficiencies and material budget. 

These are serious issues, as the CBM detector and its data acquisition system aim at unprecedented 

performance in rate capabilities and radiation hardness. 

We still follow more than one option for the technical solution of some particular tasks, such as Silicon 

pixel detectors or transition radiation detectors. Our next goal is to prepare the decision on detector 

technologies and to demonstrate the feasibility of the envisaged measurements until end of 2006. 

1

Part I 

Introduction and Overview 

3

1 Introduction and Overview 

1.1 Physics case and observables 

The exploration of the phase diagram of strongly interacting matter is one of the most challenging fields 

of modern high-energy physics [1]. Of particular interest is the transition from hadronic to partonic 

degrees of freedom which is expected to occur at high temperatures and/or high baryon densities. Both 

phases played an important role in the early universe and possibly exist in the core of neutron stars [2]. 

The discovery of this phase transition would shed light on two fundamental but still puzzling aspects of 

Quantum Chromo Dynamics (QCD): confinement and chiral symmetry breaking. In particular, at high 

baryon densities one expects new phases of strongly interacting matter [3]. The scientific progress in this 

exciting field, QCD at high baryon densities, is driven by new experimental data. 

In order to study the dynamics of strongly interacting matter far from its ground state, laboratory experiments 

are performed with high-energy nucleus-nucleus collisions. The conditions inside the transiently 

existing fireball are reflected in the abundances and phase-space distributions of the emitted hadrons. 

Important information on the early phase of the collision is provided by the quark flavor of the observed 

hadrons. In particular, hadrons containing strange or charmed quarks are regarded as sensitive diagnostic 

probes of the collision dynamics. The hadrons are either observed directly or are identified via their 

hadronic or leptonic decay products. 

Lattice QCD calculations at vanishing baryochemical potential and finite temperature predict the formation 

of a quark-gluon plasma above energy densities of about 1 GeV/fm 3 [4]. Such conditions may be created 

in central collisions between heavy nuclei already at bombarding energies of about 10 AGeV [5,6]. 

Recent lattice QCD calculations at finite baryon chemical potential predict a critical endpoint of deconfinement 

phase transition at µB ≈ 400 MeV and T≈160 MeV [7, 8]. 

Our current knowledge on the QCD phase diagram is illustrated in figure 1.1. The data points correspond 

to chemical freeze-out and result from a statistical analysis of particle ratios measured in Pb+Pb and 

Au+Au collisions at SIS, AGS, SPS and RHIC [9, 10, 11]. The solid curve along the freeze-out points 

represents a calculation with a constant baryon density (baryons + antibaryons) of about ρB = 0.75 

ρ0 [11]. The phase boundary between quark-gluon matter and hadronic matter together with the location 

of the critical endpoint as shown in figure 1.1 is predicted by lattice QCD calculations [7, 8]. These 

calculations indicate that for values of µB larger than about 400 MeV the phase transition is first order, 

whereas for µB smaller than 400 MeV there is a smooth cross over from the hadronic to the partonic 

phase (dotted line). The search for the critical endpoint is a prime goal of high-energy nucleus-nucleus 

collision experiments. 

At ultra-relativistic beam energies provided by RHIC and the future LHC, partonic matter is expected 

to be produced at very high temperatures and at small baryon chemical potentials. Similar conditions 

prevailed in the early universe. The complementary situation exists in the core of a neutron star: here 

the baryon density is very high and the temperature very low. In the laboratory, fireballs at high baryon 

densities and moderate temperatures are created at beam energies significantly below top SPS energies. 

This region of the QCD phase diagram - marked by the hatched area in figure 1.1- has not been studied 

in detail so far (see next section). 

Trajectories of nucleus-nucleus collisions in the T - µB plane have been recently calculated with a 3-fluid 

hydrodynamics model [12]. Figure 1.2 depicts the trajectories corresponding to different beam energies. 

5


temperature T [MeV] 

200 

175 

150 

125 

100 

75 

50 

25 

dilute 

hadronic 

medium 

critical endpoint 

Lattice QCD 

n b =0.12 fm -3 

quark-gluon matter 

P. Braun-Munzinger et al. PLB 518 (2001) 

F. Becattini et al. PRC 96 (2004) 

R. Averbeck et al. nucl-ex/9803001 

dense 

baryonic 

medium 

0 

0 0.2 0.4 0.6 0.8 1 

baryonic chemical potential μ B [GeV] 

Figure 1.1: The phase diagram of 

strongly interacting matter plotted as 

a function of temperature and baryon 

chemical potential. Full symbols: 

freeze-out points obtained with a statistical 

model analysis from particle ratios 

measured in heavy collisions [213, 10]. 

The critical endpoint at µB ≈ 400 MeV 

is predicted by lattice QCD calculations 

[7, 8]. "Dilute hadronic medium": 

ρB=0.038 fm −3 ≈ 0.24 ρ0. "Dense 

baryonic medium": ρB=1.0 fm −3 ≈ 6.2 

ρ0. 

According to the calculations a beam energy of 10 AGeV is sufficient to cross the phase boundary. For a 

beam energy of about 30 AGeV the trajectory is predicted to pass the region of the critical endpoint of the 

deconfinement phase transition [12]. The planned SIS 300 synchrotron at the future FAIR facility [13] 

covers an energy range of up to 45 AGeV for nuclei with A=2Z, and, hence, is well suited to explore the 

high baryon density region of the QCD phase diagram. The research programme to be performed with 

the proposed Compressed Baryonic Matter (CBM) experiment includes a comprehensive investigation 

of the relevant hadronic observables, and is focused on systematic measurements of diagnostic probes 

which have not been measured before in this energy range: the dileptonic decay of low-mass vector 

mesons and hadrons containing charmed quarks. 

, MeV 

300 

Pb+Pb, central collision 

freeze-out 

A end-point 

2 

1 

1 

10 GeV 

1fm/c 

2 

200 

2 

3 3 

4 4 4 

9 

5 5 

9 

9 

6 

9 

3 

4 

3 

2 

1 

80 GeV 

158 GeV 

100 

30 GeV 

0 

0 1000 2000 3000 

, MeV 

Figure 1.2: Trajectories of heavy ion 

collisions in the QCD phase diagram 

calculated by a 3-fluid hydrodynamics 

model [12].

1.1. Physics case and observables 7 

The major challenge is to find diagnostic probes which are connected to chiral symmetry restoration and 

to the deconfinement phase transition. A signature for the onset of chiral symmetry restoration might be 

the observation of in-medium modifications of hadrons. In-medium effects have been found for pions 

in deeply bound pionic atoms [14] and for kaons and antikaons produced in heavy-ion collisions at 

SIS energies [16, 15, 17]. Very promising observables are also short-lived vector mesons decaying into 

dilepton pairs inside the fireball. An enhanced yield of low-mass dilepton pairs has been found in central 

heavy-ion collisions [18, 19]. This observation has triggered an enormous theoretical activity on the inmedium 

modification of ρ-mesons and its relation to chiral symmetry restoration. A promising candidate 

for a probe of in-medium modifications is open charm produced at very high baryon densities [20]. 

Enormous experimental and theoretical efforts have been and are still devoted to the search for the deconfined 

phase of strongly interacting matter, the quark-gluon-plasma (QGP). The discovery of the QGP 

was announced by CERN in the year 2000 [21]. The arguments given in the press release were essentially 

based on experimental findings like enhanced production of strangeness, anomalous suppression 

of charmonium, enhanced production of low-mass dilepton pairs, and the fact that an analysis of particle 

multiplicities yields a chemical freeze-out temperature of about 170 MeV which is very close to the 

critical temperature. Several years later, new and complementary experimental results obtained at RHIC 

were interpreted as signatures of the QGP [22]. The observations of the RHIC experiments include: large 

values of the elliptic flow in agreement with hydrodynamical calculations, quenching of hadron jets, and 

quark number scaling of flow observables. 

Many signals have been proposed and are still under discussion, although the hope for finding a "smoking 

gun" has not yet become true. The discovery of the critical endpoint of the deconfinement phase transition 

would be such a direct indication for the existence of a new phase. Theoretical investigations suggest 

that particle density fluctuations occur in the vicinity of the critical endpoint, which might be observed 

experimentally as nonstatistical event-by-event fluctuations of observables [23]. This phenomenon was 

also seen in lattice QCD calculations [8]. The fluctuation signal should show up around a certain beam 

energy. 

In this section we will briefly review the existing data which are relevant for the study of the high baryon 

density region of the QCD phase diagram. 

1.1.1 Low-mass vector mesons 

The in-medium spectral function of short-lived vector mesons can be measured directly via their decay 

into dilepton pairs. Since leptons are essentially unaffected by the passage through the high-density 

matter, they provide, as a penetrating probe, almost undistorted information on the conditions in the 

interior of the collision zone [24]. 

However, dilepton pairs from vector meson decays are notoriously difficult to measure due to the very 

small branching ratios and the large combinatorial background in heavy-ion collisions. The challenge is 

to identify the electrons or muons unambiguously, measure their momentum better than Δp/p = 1% and to 

efficiently suppress the background which stems mainly from Dalitz decays and gamma conversion. The 

test of theoretical predictions requires high statistics data measured with large acceptance spectrometers 

in many weeks of beamtime. Pioneering experiments have been performed in heavy-ion collisions at 

beam energies below 2 AGeV (DLS at LBL) [18] and above 40 AGeV at CERN with the CERES setup 

[19]. The HADES spectrometer at GSI has started to take data in nucleus-nucleus collisions at energies 

up to 2 AGeV. 

The most recent result of the CERES collaboration is presented in figure 1.3 [25]. It shows the inclusive 

e + e − pair mass spectra measured in Pb+Au collisions at 40 AGeV and at 158 AGeV together with the 

hadronic cocktail of known dilepton sources and with results of model calculations based on different


assumptions on the in-medium properties of the ρ-meson. The error bars of the data at 40 AGeV - which 

contain less than 200 true dilepton pairs - are too large to rule out a particular model for the in-medium 

properties of the ρ meson. In order to draw final conclusions, a dedicated high luminosity experiment is 

required. 

-1 

) 

2 

>(100 MeV/c 

ch 

>/35 mrad 

ee 

2.1< η


1.1.3 Strangeness in dense matter 

Figure 1.4: Dimuon invariant mass 

spectrum measured in In+In collisions 

at 158 AGeV [30]. 

One of the early predictions for a QGP signal was the increased production of strangeness in the deconfined 

phase resulting in an enhanced yield of strange particles after hadronization [31]. This effect was 

expected to be even more pronounced for multistrange hyperons. Indeed, the NA57 experiment observed 

that the multiplicity of Ξ and Ω hyperons per participant is higher in nucleus-nucleus collisions than in 

proton-proton (or proton-Beryllium) collisions [32]. Moreover, the enhancement increases according to 

the s-hierarchy. Again, the interpretation of these results is still under discussion. An intriguing finding 

is that the slope of the Ω kinetic energy distribution is steeper than expected. This indicates that the 

Ω hyperon - which consists only of strange quarks - might freeze out very early. Recently, data on the 

excitation function of strangeness production measured by NA49 have revived the discussion on the role 

of strangeness as a signature for a deconfinement phase transition [33]. 

The NA49 collaboration has studied systematically the production of strange hadrons scanning the beam 

energy from top SPS energy of 158 AGeV down to 20 AGeV [34]. Figure 1.5 shows the multiplicity of 

strange particles normalized to the pion multiplicity as function of the beam energy for central Au+Au 

(Pb+Pb) collisions. The data are compared to results of hadron gas model calculations (blue lines) 

and of UrQMD transport model calculations (red lines). None of the model calculations describes the 

data set satisfactorily. The UrQMD model clearly underestimates the ratios. The reason for this is an 

overestimation of the pion yield, whereas the strange particle yield is reasonably well reproduced by 

transport calculations [35]. The statistical hadron gas model agrees roughly with the measured Λ/π ratio 

(where the AGS data do not fit well to the SPS data) and with the (Ω + + Ω − )/π ratio which has large 

error bars. 

The global trend of the excitation functions shown in figure 1.5 is understood as the consequence of 

the decreasing baryon chemical potential with increasing beam energy. In the energy range between 

AGS and SPS there is a transition from baryon dominated to meson dominated matter. The pronounced 

kink structure in the K + /π + ratio has caused speculations on a possible deconfinement phase transition 

around 30 AGeV [36]. This interpretation seems to be supported by the observation that the slope 

of the K + meson spectra exhibits a constant value within the SPS energy range. The K + slopes are 

shown in figure 1.6 in comparison to results of transport models (UrQMD and HSD) which significantly 

underestimate the data in the energy range of the kink [35]. The authors argue that the missing pressure 

should be generated in the early phase of the collision by nonperturbative partonic interactions because 

the strong hadronic interactions in the later stages - as modelled in their transport calculations - are not


〉 

+ 

π 

〈 

/ 

〉 

+ 

K 

〈 

〉 

- 

π 

〈 

/ 

〉 

- 

K 

〈 

〉 

π 

〈 

/ 

〉 

φ 

〈 

0.2 

0.1 

0.15 

0.1 

0.05 

0.008 

0.006 

0.004 

0.002 

UrQMD 2.0 

HSD 

Hadron Gas A 

AGS 

NA49 

RHIC 

1 10 10 

(GeV) 

2 

sNN 

〉 

π 

〈 

/ 

〉 

Λ 

〈 

〉 

π 

〈 

/ 

〉 

- 

Ξ 

〈 

〉 

π 

/ 〈 

〉 

+ 

Ω 

+ 

- 

Ω 

〈 

0.06 

0.04 

0.02 

0.004 

0.003 

0.002 

0.001 

0.0008 

0.0006 

0.0004 

0.0002 

UrQMD 2.0 

HSD 

Hadron Gas A 

1 10 10 

(GeV) 

2 

Figure 1.5: Excitation function of strangeness production in central Au+Au and Pb+Pb collisions. The yield of 

strange particles is normalized to the pion yield and compared to model calculations. ( [34]). 

sufficient to generate the flow [35]. 

In summary, the experimental search for discontinuities in the excitation function of strange and nonstrange 

particles has yielded intriguing results in the energy range between 10 and 40 AGeV. However, 

the data require confirmation and more comprehensive studies with reduced statistical and systematic 

errors, in particular for the (multi-strange) hyperons. 

1.1.4 Event-by-event fluctuations 

In the vicinity of the deconfinement phase transition, critical density fluctuations have been predicted to 

cause non-statistical event-by-event fluctuations of experimental observables. In particular, the study of 

event-by-event fluctuations in the hadro-chemical composition of the particle source offers the possibility 

to directly observe effects of a phase transition. Depending on the nature and the order of the phase 

transition one expects anomalies in the energy dependence of event-by-event fluctuations [23]. Ideally, 

a sudden non-monotonous change in the dynamical fluctuations measured as function of beam energy 

would be a signal of the critical endpoint. 

The NA49 collaboration has searched for dynamical event-by-event fluctuations of the (K + +K − )/(π + + 

π − ) ratio in central Pb+Pb collsions. The experimental results of a beam energy scan of dynamical 

fluctuations are shown in figure 1.7 [37] together with the analysis of UrQMD calculations. 

AGS 

NA49 

RHIC 

sNN


T (MeV) 

300 

200 

100 

+ 

K 

1 10 

string hadronic models: A+A: 

HSD 

NA49 

HSD with Cronin effect AGS 

UrQMD 2.0 

RHIC 

10 

s (GeV) 

NN 

2 

Figure 1.6: Inverse slope parameters 

of the K + spectra as function of beam 

energy measured in Au+Au and Pb+Pb 

collisions in comparison to transport 

model calculations (UrQMD, HSD). 

In order to claim a significant deviation of the data from the UrQMD event analysis one would need much 

smaller statistical and systematical errors. The errors reflect the difficulties of the measurement: (i) Due 

to the limited acceptance of NA49, the number of observed particles per event decreases with decreasing 

beam energy leading to increasing statistical fluctuations. (ii) Due to non-ideal particle identification the 

(K + + K − )/(π + + π − ) ratio is affected by the experimental dE/dx resolution and the event-by-event 

fitting procedure. 

Dynamical Fluctuations [%] 

10 

8 

6 

4 

2 

0 

+ 

(K 

- + - 

+ K )/( π + π ) 

Data 

UrQMD v1.3 

5 10 15 20 

sqrt(s) 

Dynamical Fluctuations [%] 

0 

-2 

-4 

-6 

-8 

-10 

+ - 

(p + p)/( 

π + π ) 

Data 

UrQMD v1.3 

5 10 15 20 

sqrt(s) 

Figure 1.7: Energy dependence of the event-by-event fluctuation signal of the (K + + K − )/(π + + π − ) ratio (left) 

and of the (p + ¯p)/(π + + π − ) ratio (right) as function of beam energy for central Pb+Pb collisions. The data (full 

dots) are compared to UrQMD simulations (open circles). The systematic errors of the measurements are shown 

as grey band (from [37]). 

The contributions due to finite particle number fluctuations and effect of detector resolution are estimated 

by the mixed event technique. This is a very tricky procedure and requires, for example, an excellent 

experimental event classification (i. e. definition of collision centrality, number of participants) in order 

to avoid trivial fluctuations of the kaon-to-pion ratio. 

Nevertheless, the observed increase of the fluctuation signal with decreasing beam energy indicates the


onset of a new source of fluctuations. This effect deserves further experimental and theoretical investigations. 

A future second generation experiment should have a large acceptance over a wide range of beam 

energies, and excellent capabilities for particle identification and event classification. 

1.2 The CBM experiment 

1.2.1 The research program 

The FAIR accelerators will provide heavy ion beams up to Uranium at beam energies ranging from 2 - 45 

AGeV (for Z/A = 0.5) and up to 35 AGeV for Z/A = 0.4. The maximum proton beam energy is 90 GeV. 

1.2.1.1 Experiments with proton beams 

Proton beams with energies up to 90 GeV offer the possibility to perform experiments on heavy quark 

production in p+p and p+A collisions. The production mechanisms of heavy quarks in this energy range 

are sensitive to the quark and gluon distributions of the nucleon. No data on open charm production exist 

below proton beam energies of 400 GeV, and no data on bottonium production exist below 200 GeV 

proton energy. The measurement of Ypsilon via its dilepton decay might be possible at a proton energy 

of 90 GeV (threshold beam energy for p+p→ ϒpp is Ethr ≈ 65 GeV). The prediction of b¯b cross sections 

at such a low energy is a challenge for perturbative QCD calculations. Moreover, data on the production 

of charm, strangeness and low-mass vector mesons in p+p and p+A collisions are absolutely needed as 

a reference for the results obtained in A+A collisions. The CBM detector will be well suited for the 

measurement of heavy resonances and exotica like pentaquarks produced in p+p and p+A collisions. 

The expected production cross sections for D mesons in proton induced reactions are shown in figure 1.8 

(taken from [38]). 

Figure 1.8: Production cross section 

for open charm in proton induced 

reactions. The calculation does not 

include associated charm production 

(pp→ ΛcDp) which is dominating the 

cross section close to threshold (taken 

from [38]). 

A substantial fraction of the experiments performed with the CBM detector will be devoted to proton 

induced reactions. The detection of particles containing heavy quarks - both via leptonic and hadronic 

decay channels - will benefit from the performance of the setup dictated by the heavy-ion experiments.

1.2. The CBM experiment 13 

1.2.1.2 Experiments with heavy-ion beams 

The nucleus-nucleus collisions research program of CBM will focus on the search for (i) in-medium 

modifications of hadrons in super-dense matter as signal for the onset of chiral symmetry restoration, 

(ii) a deconfined phase at high baryon densities, and (iii) the critical endpoint of the deconfinement 

phase transition. The program aims at a comprehensive study of relevant observables by systematically 

scanning experimental parameters like beam energy, system size and collision centrality. The observables 

include: 

• open and hidden charm: Charm quarks are produced in the early phase of the collision and hence 

probe high-density baryonic or partonic matter. The sensitivity of charm production to medium 

effects is enhanced due to the fact that the beam energy is close to the threshold. Charmonium 

(J/ψ mesons) will be measured via its decay into electron-positron (and muon) pairs, whereas 

D-mesons will be identified by the invariant mass of their hadronic decay products. The low 

charm production cross sections drive the request for high beam intensities (up to 10 MHz reaction 

rates) and online event selection. Neither open nor hidden charm has been measured up to now 

in nucleus-nucleus collisions at beam energies below 158 AGeV. A comprehensive experimental 

study of charm production requires a universal setup with outstanding performances in rate capability, 

radiation hardness, high-resolution secondary vertex determination, electron (muon) and 

hadron identification. 

• short-lived vector mesons decaying into electron-positron pairs: The ultimate goal is the measurement 

of the in-medium spectral function of ρ (or ω and φ) mesons in order to study effects of 

chiral symmetry restoration. This requires data with excellent statistics and low systematic errors, 

measured in the energy range from 2 - 45 AGeV. Such an amount of data can only be obtained at 

a dedicated facility. The upgraded HADES Spectrometer will be used for these studies up to beam 

energies of about 8 GeV. 

• strange and multi-strange particles: Strangeness production plays an important role as a possible 

signature for a deconfined phase of strongly interacting matter produced in nucleus-nucleus collisions 

[31]. Moreover, the properties of strange particles in dense baryonic matter are of crucial 

importance for theories explaining the inner structure of neutron stars [2]. Of particular interest in 

this respect is the hyperon-hyperon interaction which can be measured via correlations. According 

to figure 1.5, the highest hyperon densities are expected to be created in nucleus-nucleus collisions 

at beam energies between 10 and 30 AGeV. Therefore, a future experiment studying high-density 

matter should have the capability to measure efficiently (multi-) strange particles. 

• event-by-event fluctuations of observables like particle ratios, charge ratios, mean transverse momentum, 

etc.: The search for the critical endpoint of the deconfinement phase transition requires 

large acceptance and good particle identification capability over a wide range of beam energies. 

Moreover, an excellent event selection is needed. 

• elliptic flow of (multi-) strange and charmed hadrons as a probe of the early fireball. 

• photons: Offer the possibility to detect π 0 and η mesons, and direct photons emitted in the early 

and dense phase of the collisions. 

• search for exotic objects like pentaquarks, short-lived multi-strange objects [39], strongly bound 

kaonic systems [40], and precursor effects of a color super-conducting phase at very high baryon 

densities [41].


1.2.2 The detector system 

The experimental task is to identify both hadrons and leptons and to detect rare probes in a heavy ion 

environment. The apparatus has to measure yields and phase-space distributions of hyperons, light vector 

mesons, charmonium and open charm (including the identification of protons, pions and kaons) with a 

large acceptance. The challenge is to select very rare probes in Au+Au (or U+U) collisions at reaction 

rates of up to 10 7 events per second. The charged particle multiplicity is about 1000 per central event. 

Therefore, the experiment has to fulfill the following requirements: fast and radiation hard detectors, 

large acceptance and large granularity, electron and hadron identification, high-resolution secondary 

vertex determination and a high speed event-selection and data acquisition system. Figure 1.9 displays a 

sketch of the proposed setup. 

Figure 1.9: Sketch of the planned Compressed Baryonic Matter (CBM) experiment. The beam enters from 

the left hand side. The setup consists of a superconducting dipole magnet with a Silicon Tracker System inside, a 

Rich Imaging Cherenkov detector (RICH) for electron identification, three Transition Radiation Detectors (TRD), a 

Time-Of Flight (TOF) wall which is a Resistive Plate Chamber (RPC) and an electromagnetic Calorimeter (ECAL). 

The total length of the setup is approximately 12 m from target at the entrance of the dipole to the ECAL. 

In the following the detector components of the CBM setup are briefly described. 

1.2.2.1 Superconducting dipole magnet 

The dipole magnet serves for bending the charged particle trajectories and for delta-ray deflection. A gap 

of 1 m is required to provide sufficient space for the silicon tracking system. A bending power of about 

1 Tm has to be provided by the magnet in order to achieve a momentum resolution of about 1% using 

the Silicon Tracking System for trajectory determination. However, this relatively high magnetic field 

decreases the efficiency towards low momentum particles. In particular, this effect reduces the probability 

to reconstruct Dalitz decays of π 0 mesons which results in an increased combinatorial background for 

low-mass dilepton pairs. 

A lower magnetic field improves the track reconstruction for low momentum particles but worsens the 

momentum resolution obtained with the STS. However, the momentum resolution might be improved 

when taking into account the trajectory measurement with the TRDs. Simulations on momentum re-


construction using full tracking are in progress. A severe and yet unsolved problem is caused by the 

huge count rate of delta-electrons (emitted from the target) in the first Silicon Pixel detector. A possible 

reduction of the delta-electron flux onto the detectors by a shielding or an additional magnetic field is 

under investigation. 

Presently, two magnetic field configurations are under investigation: a (gaussian shaped) symmetric field 

produced by a window-frame dipole magnet with parallel pole shoes, and an asymmetric field produced 

by a dipole with inclined pole shoes. Both configurations are studied in simulations with respect to 

their performance concerning track reconstruction, momentum resolution, acceptance, and delta-electron 

deflection. 

1.2.2.2 Silicon Tracker System (STS) 

The Silicon Tracking System (STS) serves for track measurement and for determination of primary and 

secondary vertices. The current STS layout consists of minimum 7 layers and is placed inside a magnetic 

dipole field which provides the bending power required for momentum determination with an accuracy 

of Δp/p = 1 %. The STS has to fulfill the following requirements: material budget below 0.3% radiation 

length per layer to reduce multiple scattering, hit resolution of about 10 µm to achieve a vertex resolution 

of about 30 µm, radiation hardness up to a dose of 50 MRad corresponding to the dose accumulated in 

ten years of running, and read-out times of about 25 ns to accommodate reaction rates of 10 MHz. 

Silicon Pixel (Vertex-) Detectors 

The first 2 stations (distance from the target 5 and 10 cm) will consist of pixel detectors in order to 

reduce the occupancy to about 1% (for a central Au+Au collision at 25 AGeV). Monolithic Active Pixel 

Sensors (MAPS) with a pixel size of 40x40 µm 2 and a thickness of 100 µm would perfectly fulfill 

our requirements concerning vertex resolution which is needed to measure the displaced vertices of 

D mesons. However, the radiation hardness and the read out speed of nowadays MAPS detectors have to 

be improved by 1 - 2 orders of magnitude in order to match our requirements. This is the major goal for 

our R&D. Moreover, we work on track reconstruction methods to resolve the tracks and vertices of up to 

50 superimposed events in the MAPS detectors. 

As a possible alternative to the MAPS detectors based on CMOS technology we study prototypes of 

MAPS detectors realized in Silicon on Insulator (SOI) technology. Moreover, the ongoing development 

of low-mass hybrid pixel detectors for future LHC experiments may lead to a substantial reduction of 

their material budget. These detectors - which are fast and radiation hard - might then serve as a fall back 

solution for the CBM Vertex Tracker. 

Silicon Microstrip Detectors 

The third Silicon Tracking Station will consist both of pixel detectors (inner part, small forward angles) 

and of Silicon microstrip detectors (outer part, large forward angles). The last 4 Silicon layers (distance 

from the target 40, 60, 80, and 100 cm) will consist of Silicon microstrip detectors. The current layout 

foresees a pitch of 25 µm and three different strip lengths of 20, 40 and 60 mm. The strips are arranged 

such that the occupancy is below 2% for a central Au+Au collision at 25 AGeV. The detectors will be 

double sided with a stereo angle between the strips which has to be optimized by simulations. The 

ambiguities due do double (or triple) hits having a distance smaller than the strip length result in 2 (or 

6) ghost hits which pose a severe problem to the track reconstruction algorithms. It might be necessary 

to add more Silicon layers with another orientation of the strips in order to minimize the ambiguities.


Another possibility would be to reduce the length of the strips with the consequence of increasing the 

number of channels. The investigation of this problem is in progress. 

1.2.2.3 Ring imaging Cherenkov detectors (RICH) 

The RICH detector is designed to provide identification of electrons and suppression of pions in the 

momentum range of electrons from low-mass vector-meson decays. The actual layout of the RICH 

detector consists of a radiator (3 gases under study: N2, 40% He + 60 %CH4, and 50% N2 + 50 %CH4, 

length 2.2 m), a mirror (3 mm Be covered with 0.5 mm glass, radius 4.5 m), and a photon detector 

composed of about 100.000 photomultiplier (PM) channels (6 mm diameter, quantum efficiency 20 %). 

The glass window of the PMs is covered with wave-length shifter (WLS) films in order to increase the 

absorption of Cherenkov photons. Depending on the detection threshold at small wavelengths λmin, the 

number of measured photons per ring ranges from 15 (λmin = 250 nm) to 33 (λmin = 120 nm). The 

corresponding figures of merit are N0 = 138 and 292, respectively. The expected excellent efficiency of 

the PM-WLS combination for Cherenkov photons has still to be confirmed by test measurements. 

According to simulations based on UrQMD and GEANT, about 90 rings are produced in a central Au+Au 

collision at 35 AGeV. However, this number strongly depends on the detector geometry which still needs 

to be optimized. The crucial task is to match the rings to the charged particle tracks. If the track position 

at the mirror can be determined with an accuracy of 200 µm, and assuming a momentum resolution of 

1%, the mismatch of pions to electron rings is less than 10 −3 per event. This number will be considerably 

improved when taking into account particle identification by time-of-flight measurement and by the TRD. 

Performance studies are in progress, taking into account track reconstruction and more realistic detector 

properties, in order to optimize the detector layout. 

1.2.2.4 Transition radiation detector(TRD) 

Three Transition Radiation Detector stations will serve for particle tracking and for the identification of 

high energy electrons and positrons (γ > 2000) which are used to reconstruct J/ψ mesons. According to 

simulations which are based on the experience obtained with the development of the TRD for ALICE 

and of the TRT for ATLAS, pion suppression factors of up to 200 (for momenta above 2 GeV/c) at an 

electron efficiency of better than 90% can be achieved. In order to provide sufficiently good particle 

tracking the position resolution should be on the order of 200 µm. 

The major technical challenge is to develop highly granular and fast gaseous detectors which can stand 

the high-rate environment of CBM in particular for the inner part of the detector planes covering forward 

emission angles. For example, at small forward angles and at a distance of 4 m from the target, we expect 

particle rates of more than 100 kHz/cm 2 for 10 MHz minimum bias Au+Au collisions at 25 AGeV. In a 

central collision, particle densities of about 0.05/cm 2 are reached. In order to keep the occupancy below 

5% the size of a single cell should be about 1 cm 2 . 

Within our R&D activities we investigate both the ALICE-TRD and the ATLAS-TRT concept. Both 

detector versions have to be modified in order to meet the CBM requirements. In the case of the ALICE- 

TRD, the rate capability of the read-out chambers has to be improved. Various prototypes of fast Multi 

Wire Proportional Chambers (MWPC) and Gas Electron Multipliers (GEM) have been tested with proton 

and pion beams up to intensities of 100 kHz/cm 2 and no major deterioration of the performance has been 

observed. The high-rate performance is required for less than 30% of the active TRD area (which is about 

500 m 2 in total). A detector concept for CBM has been studied on the basis of straw tubes (ATLAS-TRT 

option). Here, the challenge is to build very small straw tubes which still provide a high efficiency in the 

regions of highest occupancy.


1.2.2.5 Resistive plate chambers (RPC) 

An array of Resistive Plate Chambers will be used for hadron identification via TOF measurements. The 

TOF wall is located about 10 m downstream of the target and covers an active area of about 120 m 2 . The 

required time resolution is about 80 ps. For 10 MHz minimum bias Au+Au collisions the innermost part 

of the detector has to work at rates up to 20 kHz/cm 2 . At small deflection angles the pad size is about 

5 cm 2 corresponding to an occupancy of below 5% for central Au+Au collisions at 25 AGeV. With a 

small-size prototype a time resolution of about 90 ps has been achieved at a rate of 25 kHz/cm 2 . Future 

R&D concentrates on the rate capability, low resistivity material, long term stability, and realization of 

large arrays with overall excellent timing performance. 

1.2.2.6 Electromagnetic calorimeter 

The electromagnetic calorimeter will be used to measure direct photons, neutral mesons decaying into 

photons, electrons and muons. Simulations and R&D have been started based on the shashlik type of 

detector modules as used in HERA-B, PHENIX and LHCb. Particular emphasis is put on a good energy 

resolution and a high pion suppression factor. 

1.2.2.7 Diamond microstrip detector 

A microstrip CVD diamond detector will be used to generate the start signal for the time-of-flight measurement. 

These detectors are fast and radiation hard and can be operated at particle intensities of up to 

10 9 ions/s. 

1.2.2.8 Forward calorimeter for event characterization 

A very precise characterization of the event class is of crucial importance for the analysis of eventby-event 

observables. The experimental determination of collision centrality requires the simultaneous 

measurement of the charged particle multiplicity and of the energy carried away by the spectator fragments 

at forward angles. The design of a forward calorimeter for CBM will start in the near future. 

1.2.2.9 The DAQ and Event Selection System 

The measurement of open charm is the key factor shaping the architecture of front-end electronics, data 

acquisition, and event processing in CBM. The D-mesons will be identified via the displaced vertices 

of their hadronic decays. The decision for selecting candidate events thus requires tracking, primary 

vertex reconstruction, and secondary vertex finding in the STS at the full interaction rate of 10 MHz. 

The execution time of such complex algorithms will vary strongly depending on the centrality of the 

interaction. This does not match well with the conventional system design using triggered front-end 

electronics, where the information of an event can be held for a limited time, usually only a few µs, in 

the front-end until a first level trigger decision prompts the data acquisition system to collect all data of 

an event and transport it to further processing stages. 

In CBM we adopt a different approach. The front-end electronics of all detectors will be self-triggered. 

Each particle hit is autonomously detected and the measured hit parameters are stored with precise time 

stamps in large buffer pools. The event building, done by evaluating the time correlation of hits, and 

the selection of interesting events is then performed by processing resources accessing these buffers via 

a high speed network fabric. The essential performance factor is now computational throughput rather


than decision latency, which results in a much better utilization of the processing resources especially 

in the case of heavy ion collisions with strongly varying multiplicities. Since there are no dedicated 

trigger data-paths, all detectors can contribute to the event selection from the first decision level on. The 

communication and processing resources can be configured to select relevant events for a wide range of 

physics signals, ranging from D and J/ψ detection in A-A collisions over low-mass dilepton detection in 

p-A collisions to ϒ detection in p-p collisions at up to 500 MHz interaction rate. 

This approach is very data intensive. In CBM the data flow from the front-ends to the processing resources 

will be about 1 TByte/sec, and becomes feasible at affordable cost due to continued strong 

progress in the communication and data processing technology. The key R&D areas for CBM are the 

development of self-triggered front-end electronics with adequate output bandwidth, of a data processing 

system which allows to combine the advantages of hardware processors build from programmable logic 

and software processors, and last but not least, of highly efficient feature extraction and event selection 

algorithms adapted to such a processing environment. 

1.2.2.10 The HADES Detector 

The HADES spectrometer will be installed in the CBM cave to measure dileptons and hadrons at beam 

energies up to about 8 AGeV. After the planned upgrade of the HADES TOF detector its granularity is 

sufficiently large to cope with the high particle multiplicity at forward angles. These measurements can 

be performed before the installation of the CBM detectors is completed. 

1.2.3 Detector performance 

The performance of the CBM detector system is studied by Monte Carlo simulations using the event 

generators UrQMD and PLUTO, the transport codes GEANT3/4 and FLUKA, and the software packages 

VMC and CBMroot. The development of the simulation framework CBMroot was one of the major 

achievements in 2004. The feasibility studies concentrate on the development of 

- track and momentum reconstruction routines, 

- hadron identification, 

- electron identification, 

- trigger studies for open and hidden charm, 

- analysis of dilepton pairs from the decay of vector mesons (ρ,ω,φ,J/ψ), 

- identification of D mesons, 

- identification of hyperons, 

- event-by-event fluctuations of hadron ratios, 

- digitizers for the various detector systems. 

Realistic track reconstruction routines are still under development, and have not been used up to now for 

simulations on particle identification. The feasibility studies performed so far are based on assumptions 

on track reconstruction efficiency, momentum resolution, detector efficiencies, and particle identification 

probability. Therefore, the results of the studies on dilepton pairs, D-mesons, hyperons, and hadrons are 

still very preliminary. 

Moreover, no reconstruction of complete particle trajectories from target to ECAL has been performed 

up to now. This requires matching of hits in the STS, RICH, TRDs, RPC, and ECAL. Such a full tracking


is needed in order to optimize the number and relative distance of detector stations, and the granularity 

and position resolution of the detectors. Hence, the detector layout presented in figure 1.9 is still generic, 

and will very likely be modified as a result of future tracking studies. 

Geometrical acceptance 

The dimensions of the detectors are chosen such that for central nucleus-nucleus collisions at 25 AGeV 

and a magnetic bending power of 1 Tm more than 60% of the emitted charged particles are accepted. 

The acceptance does not change much as function of beam energy. 

Track reconstruction 

The track reconstruction algorithms for the Silicon Tracking System (STS) operated in the magnetic 

dipole field are based on methods like conformal mapping, Kalman filter, and Hough transform. The 

main challenge is the high track density, in particular for the first two stations which will consist of pixel 

detectors. Actually, the track reconstruction efficiency is better than 90% for particle momenta above 

1 GeV/c, but it drops rapidly towards lower momenta. The momentum resolution is better than 1 % 

at momenta above 1 GeV/c. The primary (secondary) vertex resolution is about 1 (10) µm in x and 

y, and about 6 (60) µm in z (beam) direction. The track reconstruction efficiency might be improved 

by lowering the magnetic field which bends the low momentum particles out of acceptance. A lower 

magnetic field will reduce the momentum resolution, which, however, might be improved also by taking 

the TRD as tracking stations into account. The optimization of the track and momentum reconstruction 

routines are in progress. 

Moreover, the algorithms do not yet take into account a realistic granularity of the STS stations. The 

finite hit resolution is still modelled by position smearing with a variance of σ = 10 µm. This seems 

reasonable for the pixel stations, but is inadequate for the four strip stations which have essentially a 

one-dimensional resolution only. Studies are on the way to find the optimum geometry (strip length, 

stereo angle) for the double-sided Silicon strip detectors, and the optimum number of tracking stations. 

Hadron identification 

The identification of hadrons is based on time-of-flight measurements with the RPC which is located 

10 m downstream from the target. Based on the assumption of an overall time resolution of 80 ps, pions 

can be separated from kaons up to momenta of about 4 GeV/c with a purity of almost 100 %. For higher 

momenta up to about 7 GeV/c, the separation can be performed by unfolding the mass distributions using 

a 2D probability density function. Protons can be separated from kaons up to 7 GeV/c with almost 100% 

purity. 

Low-mass vector mesons 

The challenge of electron-positron pair spectroscopy at low invariant masses is the large combinatorial 

background caused by π 0 Dalitz decays and gamma conversions (the gammas mainly stemming from 

π 0 decays). The most efficient way to reduce this background is to reconstruct the pions and gammas 

and to remove the corresponding electron-positron pairs from the data sample. However, this method 

requires a large acceptance for soft electrons/positrons. Another source of background are pions which 

are misidentified as electrons. This background becomes important if the pion suppression factor is 

smaller than 10 4 . This value can be achieved with RICH and TRD. 

For the feasibility studies of dilepton measurements we have started to take into account STS track 

reconstruction and electron identification. Due to the still low track reconstruction efficiency for lowmomentum 

electrons, the reconstruction of π 0 Dalitz decays and gamma conversions is not yet very


efficient in background reduction. On the other hand, the simulations demonstrate that the signal to 

background ratio can be significantly improved if the tracking efficiency is enhanced for low momentum 

particles. 

The measurement of low-mass electron-positron pairs requires an optimized setup, and, therefore, will 

most probably not be performed simultaneously with the measurements of the hadronic observables. 

Possible modifications presently under investigation are the lowering of the magnetic field strength to 

improve the acceptance for low-momentum electrons, the shift of the target into a field-free region, and 

the optimization of the Silicon Tracker configuration to reduce multiple scattering. 

Hidden charm 

Charmonium will be identified via its dileptonic decays. In the case of electron-positron pair measurements 

the combinatorial background is dominated by π 0 Dalitz decays, gamma conversion, and misidentified 

pions. Most of the background pairs are efficiently rejected by requiring a minimum transverse 

momentum of 1 GeV/c for single leptons. For Au+Au collisions at 25 AGeV the signal to background 

ratio is found to be about 3. However, this result is still preliminary as the feasibility studies are not 

based on reconstructed tracks and momenta. 

Simulations of J/ψ identification via the measurement of dimuon pairs have been started. In this case the 

combinatorial background is caused by muons from the decay of charged pions and kaons. One possible 

option is to identify the muons with the ECAL which is located about 11 m downstream of the target. At 

this distance about 10% of the pions and about 50% of the kaons have been decayed, and the resulting 

signal to background ratio is below 0.1 for Au+Au collisions at 25 AGeV. The background can be further 

suppressed by identifying and rejecting trajectories with a kink caused by the weak decays. This requires 

a realistic track reconstruction method. 

Simulations are in progress to study the generation of a trigger based on electron-positron pairs with high 

single transverse momenta (pt above 1 GeV/c for each particle) using the TRD stations only. 

Open charm 

The feasibility studies concentrated on the identification of D 0 mesons via the hadronic decay into a pion 

and a kaon. The challenge is to achieve the required secondary vertex resolution in order to suppress efficiently 

the background generated from prompt mesons, but also from secondary particles like hyperons 

and neutral kaons. The background is simulated by transporting UrQMD events through the STS using 

GEANT4. The simulations include track reconstruction without magnetic field (straight line approximation 

using Kalman filter). This method allows to study the influence of multiple scattering caused by the 

STS stations on the secondary vertex resolution. 

The combinatorial background in the invariant mass spectrum of the kaon-pion pair is reduced by requiring 

kinematical conditions like a minimum impact parameter of the single particle tracks in the target 

plane and a minimum displacement of the secondary vertex in beam direction. Particle identification is 

required in order to obtain a signal to background ratio better than one. 

According to the simulations done so far, the kinematical cuts on the displaced vertex of pions and kaons 

reject most of the events which do not contain a D meson. An event rejection factor of about 1000 can 

be achieved, reducing the primary reaction rate of 10 MHz to a level of 10 kHz which can be archived. 

The next step is to perform a realistic tracking in the magnetic field. Moreover, we will study the feasibility 

to identify charged D mesons which decay into two pions and a kaon.


Hyperons 

The reconstruction of hyperons from UrQMD events using the STS tracking stations has been studied by 

two approaches. In one simulation the magnetic field was neglected, and the tracks including secondary 

vertices were reconstructed by straight line fits. Moreover, ideal particle identification was assumed. 

In this study Lambda, Xi and Omega hyperons were reconstructed almost without background. The 

acceptances depend strongly on the requirement of proton and pion identification (i.e. on the acceptance 

of the TOF wall). The second approach is based on the assumption of ideal track and vertex finding. 

Track fitting and momentum reconstruction is performed in the inhomogeneous magnetic field of the 

dipole. The Lambda is reconstructed without identification of the decay products with an efficiency of 

about 20 %. The next step is to include full track and vertex reconstruction with the STS in the magnetic 

field. 

Event-by-event fluctuations and collective flow 

Due to the large geometrical acceptance and the good particle identification capability the CBM detector 

is well suited to study bulk properties of the fireball like particle abundances and collective flow 

of particles. Simulations have been performed to study non-thermal event-by-event fluctuations of the 

kaon-to-pion ratio. Taking into account geometrical acceptance and hadron identification, the event-byevent 

K/π ratio in Au+Au collisions at 25 AGeV as given by URQMD can be accurately reproduced. 

This means that the experimental contributions to the width of the K/π distribution are very small and 

dynamical fluctuations can be measured. Simulations on this subject are in progress. As the geometrical 

acceptance of the setup varies only little with beam energy one can perform an accurate energy scan of 

fluctuation observables to search for the critical endpoint of the QCD deconfinement phase transition. 

1.2.4 Planning and organization 

Schedule 

This Technical Status Report still includes alternative possible technical solutions for the CBM subsystems. 

It will take another two years of intensive R&D in order to decide upon the optimum layout of the 

experiment, the technology of the detectors, and on the FEE, DAQ, and online event selection. We plan 

to submit a Technical Proposal at the end of 2006. The detailed design of the CBM subsystems including 

prototyping leading to the Technical Design Reports will take another 3-5 years. For the construction of 

the components we foresee 2-3 years. Installation of the first detector components in the CBM cave will 

start in 2012. Commissioning of the experiment is planned for 2014 when the operation of SIS300 starts 

according to the present schedule. 

Responsibilities 

Currently, the CBM collaboration consists of 42 institutions (including applications) and of more than 

300 persons. The organization of the CBM collaboration actually consists of a Collaboration Board 

with 39 members, a Technical Board with representatives of 12 subprojects, and 10 physics performance 

working groups. 

The different tasks and subsystems of CBM are divided into working packages which are attached to 

working groups. This preliminary sharing of the responsibilities is based on the interest expressed by 

the institutes taking into account their expertise and ongoing R&D activities. The assignment of the 

responsibilities is neither complete nor final. In particular, we search for new collaborating institutes to


participate in the activities and to take over responsibilities. 

Several groups and institutions which are not (yet) member of the CBM collaboration work with us on 

various CBM related R&D projects within the framework of EU funds (FP6 Hadron Physics and INTAS). 

Cost estimates 

Wherever possible, the costs of the CBM detectors have been mostly estimated on the basis of similar 

detectors of LHC experiments. There are still uncertainties due to different detector options. The approximate 

costs are 42 - 51 M for the detectors and the magnet, 7-9 M for data acquisition and 

computing, and 5 M for the infrastructure.

Part II 

Detector Systems 

23

2 The Silicon Tracking Station (STS) 

2.1 Introduction 

The CBM spectrometer concept foresees high-resolution tracking of all charged particles directly after 

the target. A magnetic dipole field of Bmax = 1.5 T will provide a momentum kick of p ≈ 0.3 GeV/c 

over the full extension of the tracking station. The main tasks of the tracking station can be summarized 

as follows: 

• track reconstruction for all charged particles with momentum above 0.1 GeV/c and with a momentum 

resolution of order 1% at 1 GeV/c, 

• primary and secondary vertex reconstruction with a resolution good enough to efficiently reconstruct 

open charm production, 

• V0 track pattern recognition throughout the tracking station for reconstruction of weak decays and 

recognition of electron-positron pairs from photon conversion. 

A particular feature of the CBM spectrometer is its high rate capability. To reach the design goal of 

10 million reactions per second a high performance read-out system has to provide freely streaming 

detector information with time stamps for event synchronization. 

2.1.1 Tracking in CBM 

The tracking philosophy in CBM is to achieve momentum measurement before particle-id detectors 

are traversed. This keeps the size of the tracking station compact and permits to use silicon detectors 

throughout. The technology and high position resolution of silicon detectors allow to constrain the 

tracking to inside the magnetic field volume where bending power is provided. Moreover, a compact 

field volume is of economical interest since super conducting technology can be used to provide a high 

field over a comparatively short length and with a moderate gap height. 

Besides a good momentum resolution also the two-track separation capability is of high importance for 

track reconstruction with high efficiency and purity. Due to the very high track density high granularity 

is mandatory. Close to the beam axis and near the target, pixel detectors are the only choice. In the other 

regions strip detectors will provide sufficient granularity. High position resolution is required for efficient 

track reconstruction, good momentum resolution up to 10 GeV/c and secondary vertex resolution below 

50 µm. 

The Silicon Tracking Station (STS) will not contribute dE/dx information for particle identification. 

Particle identification for hadrons will mainly be provided by time-of-flight measurements. For lepton 

identification Cherenkov and transition radiation detectors as well as an electromagnetic calorimeter 

are foreseen. Hence, tracks reconstructed in the STS have to be matched to hits in the PID detectors. 

Additional large area tracking detectors will improve the track reconstruction performance in this respect. 

The current concept of the CBM spectrometer assumes that this information is provided by the distributed 

transition radiation detectors. 

25


2.1.2 Operational environment 

A central heavy ion reaction of Au + Au at 25 AGeV produces 1400 charged particles and more than 600 

hundred energetic photons in the final state. Averaged over all impact parameters the particle multiplicity 

is about a factor 4 smaller than the maximum multiplicity. Strong kinematical focusing of reaction 

products into a narrow forward angle results in extremely high track densities. In addition, δ-electrons, 

showing a nearly exponential momentum distribution, are produced by each beam particle traversing the 

solid target. Although most of it are curled up by the magnetic field at the target area, a substantial yield 

of electrons will reach the first tracking station. 

2.1.3 Overall geometry 

Track information has to be provided for all charged particles which are identified in the particle-id 

detectors behind the large gap dipole magnet. To achieve this, the magnetic field volume is filled with 

a minimum of 7 tracking stations, each providing true two-dimensional tracking information. These 7 

stations are positioned at 5, 10, 20, 40, 60, 80 and 100 cm downstream the target around the beam axis. 

It has to be verified, that this geometry will provide the redundancy required by the tracking algorithms 

to achieve sufficient track reconstruction efficiency. To maximize the vertex reconstruction resolution, 

the first three tracking stations will be placed inside a vacuum vessel which will also contain the target 

assembly. 

The exponentially dropping transverse momentum of particles emitted from the reaction zone is reflected 

in a steeply falling track density as the radial distance from the beam axis increases. Consequently, to 

cover as much of the phase space as possible, the tracking detectors have to reach as close as possible 

to the beam axis. On the other hand, the inner sections of the tracking stations have to be mounted on a 

moveable support structure to permit removing the active areas from the beam line during beam tuning. 

We aim in a design in which the inner hole of the first three tracking stations is adjustable. This would 

allow to optimize the acceptance for different collision system sizes and beam energies. 

Plane D [cm] xmax [cm] ymax [cm] Area [cm 2 ] 

1 5 2.5 2.5 25 

2 10 5 5 100 

3 20 10 10 400(100) 

4 40 20 20 1600 

5 60 30 30 3600 

6 80 40 40 5600 

7 100 50 50 10000 

Table 2.1: Geometrical properties of the silicon tracking station. D is the distance from the target, xmax and 

ymax the maximum extension in vertical and horizontal direction, respectively. The value in brackets refers to the 

amount of pixels placed in the vacuum. 

In the peak field region of the magnet, the transverse momentum kick between two tracking stations is 

of order 50 MeV/c. This translates to a horizontal displacement from one to the next station of typically 

a few millimeter depending on the particle momentum. In a tracking station with a material budget of 

6 · 10 −3 X0 (a typical number for LHC silicon tracking stations) the relative error in the measurement of 

the displacement due to deflection in the magnetic field is ≈ 1.5 %. Assuming a position resolution of 

10 µm or better, the uncertainty in momentum measurement will be dominated by multiple scattering 

for most of the particles of interest (see Sec. 12.2). Moreover, minimal material budget is essential to 

minimize the secondary particle production, a critical performance issue for the trigger capability of the

2.2. The inner tracking station (ITS) 27 

spectrometer. Hence, low material budget is a general request to all tracking stations. 

The silicon tracking station is subdivided into two sections. The inner tracking station is optimized 

for precise vertexing and will be operated in vacuum. It will comprise 3 out of the 7 stations and will 

essentially be composed of silicon pixel detectors. The main tracking station will contain strip sensors 

with varying strip geometry to accommodate for the steeply varying local hit multiplicities. The gross 

geometrical properties are summarized in Tab. 2.1. 

2.1.4 Status of R&D and design optimization 

Silicon tracking detectors are widely used in all kind of nuclear and high energy physics experiments and 

also in numerous medical applications. Hundreds of man-years went into the R&D of position sensitive 

sensors in the past decade. As a matter of fact, the CBM collaboration will heavily rely on developments 

achieved to implement the upcoming LHC experiments with silicon vertex detectors. Moreover, recent 

R&D efforts for future high energy physics experiments are aiming on similar performance parameters 

like they are dictated by the needs of the CBM experiment. In this respect, two research avenues are of 

great importance for the CBM experiment: 

• Monolithic sensor technology for pixel trackers. With this technology, highest position resolution 

at a minimum material budget seems in reach. 

• Thinning technologies to produce read-out chips and in particular also double-sided strip sensors 

with optimized material budget. 

At the time of this report, dedicated R&D on silicon detectors for the tracking station of the CBM experiment 

has not been started. Hence, in the following we report the first design concept which will be the 

basis for detailed simulations including realistic detector responses. An important criterium to achieve 

the physics goals is the material budget of in particular the inner tracking station. The efficiency for open 

charm reconstruction steeply rises with the secondary vertex resolution. Hence, the ITS is ideally constructed 

from ultra-thin high resolution sensors like they would be provided by monolithic sensors which 

combine the sensor material and the read-out structure in the same substrate. This lead us to closely 

collaborate with colleagues developing monolithic sensors for a future linear collider experiment. Most 

of the physics performance studies reported in chapter III are based on the anticipated performance of 

such sensors. However, concerning radiation tolerance and read-out speed there is still a gap between the 

performance currently achieved and the one needed for the ITS. A fall-back solution are hybrid detectors 

as used in many experiments today. The proposed NA48 upgrade foresees a next generation hybrid 

detector (giga tracker) which is close in performance to the ITS needs. 

2.2 The inner tracking station (ITS) 

2.2.1 Design aspects 

The high track density on the tracking station close to the target demand detectors with ultra-high granularity 

above 10.000 cells per cm 2 in this region. In particular the first station behind the target faces 

an additional load due to δ-electrons emerging from the target. True two-dimensional pixel detectors 

are chosen for this region to avoid large contribution from ghost tracks during event reconstruction. An 

essential requirement for the inner tracking station is a high secondary vertex resolution to reconstruct 

two body decays of D-mesons. With typical laboratory momenta of the decay particles of a few GeV/c 

the material budget of the tracking detectors is a big concern. Therefore, the technology of desire are


X 

5 cm 5 cm 10 cm 

Figure 2.1: Configuration of the hybrid pixel modules around the beam line composing the three tracking stations 

of the ITS. The outer part of station 3 is not displayed. 

monolithic pixel detectors. The most promising development in terms of minimal material budget and 

resolution are Monolithic Active Pixel Sensors as described in the following section. With Pixel sizes as 

small as 25 × 25 µm and an anticipated sensor thicknesses of 50 µm or below, such sensors promise outstanding 

tracking performance. As a matter of fact, to fully exploit such a performance, the ITS section 

of the tracking station will have to be placed in vacuum. The geometry described in the following chapter 

was derived to allow a realistic simulation including digitization of the GEANT track information 

to mimic the detector response. It has to be seen as a generic configuration which does not yet include 

realistic detector infrastructure. 

1 cm 

5 cm 

Figure 2.2: Configuration of the two types of pixel modules to form the geometry of station 1 (left panel) and 2, 

and the inner part of station 3 (right panel). The line types indicate different locations in the direction of the beam 

to allow partial overlap (staggering). 

We foresee to place the first two tracking stations completely inside the vacuum. For the third station 

only the inner section is placed inside. The outer part can also be made from silicon strip sensors and 

can be be situated outside the vacuum vessel. Such a configuration, as it is shown in Fig.2.1, allows 

9 cm 

1


to keep the vacuum vessel small and consequently also to use a thinner exit window. To constrain the 

number of different geometries we have worked out a configuration which uses only two different module 

geometries (see Fig. 2.2). L-shaped modules are forming the part closest to the beam axis, a rectangular 

shape is used for the outer region farer away from the beam axis. Such a configuration would allow to 

move the tracking stations in two parts away from the beam spot during focusing. The final positioning 

could be tuned to the level of radiation by eventually leaving a gap between the left and the right section 

of the sensor planes. The large symmetry in the system also allows to replace pixel modules once they 

suffer from radiation damage. In the closest position, the hole kept open for the beam particles has an 

area of 1 cm 2 , and a minimal distance between sensor and beam axis of 5 mm. 

2.2.2 Conceptional design 

In total 6 pixel L-shaped modules and 12 rectangular modules are needed to build the first three stations 

of the vertex tracker located in the vacuum. As a generic monolithic pixel module we assume a chip of 

dimension 5×10mm 2 . The sensitive part is composed of 50176 pixels of dimension 25×25 µm arranged 

in 392 columns with 128 pixels each. The other area contains the read-out infrastructure to process the 

columns in parallel. A pixel module is produced by mounting to the front side and back side pixel chips 

in such a way, that in transverse direction the coverage with active sensors is 100%. Such a configuration 

is shown in Fig. 2.3. A multi-layer hybrid flexible printed circuit will be placed on either surface to read 

out the pixel chips. For simplicity, it is not shown in the figure. The substrate will have to be actively 

cooled by flushing a cooling agent. 

Figure 2.3: Placement of the MAPS sensors on the substrate to achieve full coverage with active pixel. In this 

configuration 24 MAPS chips are needed on each side. The total number of pixels for a module of this geometry 

amounts to 2 million. 

2.2.3 Monolithic Active Pixel Sensors (MAPS) 

2.2.3.1 Introductory remarks 

The physics goals of the CBM experiment call for unprecedented flavour tagging performances, out of 

reach of established technologies in their present shape: Charge Coupled Devices (CCD) provide the 

necessary granularity and may be thinned down to the thickness wanted, but are too slow and radiation


sensitive; Hybrid Pixel Sensors, on the contrary, are fast and radiation tolerant but suffer from modest 

granularity and significant material budget. 

The emergence of CMOS sensors (also called MAPS 1 ) for charged particle tracking offers new perspectives 

in high precision vertexing and may provide the required performances. Their development, which 

started a few years ago, has demonstrated that this novel technology provides charged particle detection 

with excellent detection efficiency and spatial resolution. More recently, the issues of fast read-out, radiation 

tolerance and thinning were addressed. Together with the search for optimal fabrication processes, 

they constitute the main R&D directions of the coming couple of years. 

The main features of CMOS sensors and of their demonstrated performances, based on the MIMOSA 2 

prototype series, are summarised hereafter, emphasizing the most recent progresses. An outlook of the 

R&D programme towards the coming three years is provided next, followed by an estimate of the budget 

needed to achieve it. 

2.2.3.2 Main features of the technology 

CMOS sensors are manufactured in standard CMOS technology, which offers low fabrication costs and 

fast turn-over for their development. The key element of this novel technology is the use of an n-well/pepi 

diode to collect, through thermal diffusion, the charge generated by the impinging particle in the thin 

(typically 5 - 15 µm) epitaxial layer underneath the read-out electronics [42]. The detection of minimum 

ionising particles is illustrated on Fig. 2.4. 

An attractive peculiarity of the sensors is that they allow to fabricate System-on-Chips (SoC) by integrating 

signal processing micro-circuits (amplification, pedestal correction, digitisation, discrimination, 

etc.) on the detector substrate. Moreover, the latter may be thinned down to a few tens of microns since 

the active volume is less than 20 µm thick. 

Figure 2.4: Charged particle detection mechanism within a CMOS sensor. 

2.2.3.3 Detection capabilities and spatial resolution 

The ability of these sensors to provide charge particle tracking is now well established [43]: several 

prototypes exploring various manufacturing processes and key parameters of the charge sensing, demonstrated 

that a detection efficiency of 99 % and a single point resolution of 2 µm could regularly be 

1 standing for Monolithic Active Pixel Sensor 

2 standing for Minimum Ionising MOS Active pixel sensor


achieved, based on a pixel pitch of about 20 µm. It was also shown that digitising the charge on a small 

number of ADC bits (e.g. 3 bits) would not degrade the resolution beyond ∼ 2.5 – 3 µm. The double hit 

resolution was also studied, and found to be ∼ 30 µm. 

Most of the R&D was performed with small (few mm 2 wide) prototypes made of a few thousand pixels. 

A reticle size (i.e. ∼ 3.5 cm 2 ) sensor, composed of ∼ 1 million pixel, was also fabricated. Called 

MIMOSA-5, it was manufactured in batches of 6 inch wafers made of 33 sensors, as shown on Fig. 2.5. 

Figure 2.5: Wafer of reticle size sensors (left) and zoom on individual chips (right). 

The results of its tests at the CERN-SPS confirmed the performances obtained with the small prototypes: 

a detection efficiency exceeding 99 % and a single point resolution better than 2 µm were observed. 

This sensor is now being used for various purposes, ranging from thinning investigations to applications 

of low energy electron imaging (electronic microscopy, oncotherapy beam monitoring, etc.). 

2.2.3.4 The challenges of speed, radiation tolerance and material budget 

A) Read-out speed 

Fast read-out strategy and obstacles: The generation of sensors developed until 2002 was not foreseen 

to allow very fast signal processing, but rather to assess the detection capabilities and the spatial 

resolution of the technology. This first generation of sensors happens however to be already well suited 

to applications requiring frame read-out times of 1 millisecond. It fits, for instance, the requirements 

of the vertex detector upgrades foreseen in the RHIC experiments in order to take advantage of the luminosity 

increase and beam pipe radius reduction planned in the coming couple of years. The use of CMOS 

sensors in the CBM experiment, on the other hand, is much more demanding and imposes to shorten the 

read-out time by two orders of magnitude, a goal which calls for a vigorous R&D programme. 

The general idea leading to fast sensors consists in splitting them in numerous sub-arrays processed in 

parallel. This approach translates into a tremendous data flow, close to one Tbit/s/cm 2 . The achievement 

of a high read-out speed therefore strongly depends on the possibility to implement on-chip hit 

recognition and data sparsification. 

On-chip processing is complicated by the smallness of the signal amplitude (in the range of millivolts), 

which is not far from natural dispersions occuring in CMOS processes. Another handicap comes from 

the constraint of the technology: N-wells bound to serve as charge collecting diodes, a feature which


hampers the use of P-MOS transistors to treat the signal charge inside the sensor sensitive area. As a 

consequence, signal treatment inside pixels is essentialy limited to functionalities achievable with N- 

MOS transistors, those relying on P-MOS transistors being performed at the sensor edge, where no 

charge collection takes place. 

Since the charge collection time is dictated by thermal diffusion, and amounts to a few tens of nanoseconds, 

the ultimate read-out time achievable lies in the range of a few microseconds. The goal of the R&D 

is to reach this ultimate value. 

Present achievements: Since 2002, three different prototypes were fabricated in order to explore various 

charge collection and signal treatment architectures integrated in sensors adapted to fast, massively 

parallel, read-out. These architectures are based on the concept of pedestal subtraction inside each pixel 

(via correlated double-sampling) and on grouping the pixels in short columns read out in parallel, each 

column being equipped with a single discriminator handling the signals coming out sequentially from all 

pixels of the column. 

The first of this series of prototypes was MIMOSA-6 [44]. Each of its pixels was equipped with charge 

amplification micro-circuits and allows for correlated double sampling (CDS) operation in order to subtract 

the pedestal associated to the average leakage current integrated by each sensing device. The chip is 

organised in columns grouping 128 pixels (28 µm pitch); its read-out time is close to 25 µs. A comparator 

is integrated at the end of each column for discrimination purposes. Tests of the prototype showed very 

good noise performances at the single pixel level (i.e. ∼ 15 e − ENC during the read-out phase) as well 

as an acceptable dispersion of the discrimination thresholds. However, substantial additional noise was 

observed, suspected to originate from cross-talks between the digital and analog circuits inside the pixels 

and from the dispersion of the pixel characteristics inside each column. 

The next generation of fast prototypes (i.e. MIMOSA-7 and -8) were fabricated in 2003 and 2004 respectively. 

Both chips were tested with a 55 Fe source in 2004. Particularly encouraging results were obtained 

with MIMOSA-8 [45] (see Fig. 2.6). Manufactured in TSMC-0.25 µm technology, its architecture is 

inspired from that of MIMOSA-6: it features in-pixel amplification and CDS, and is organised in 4 subarrays 

of 32 columns read out in parallel. Each column contains 32 pixels (25 µm pitch) and is ended 

with a discriminator. Three sub-arrays are based on a pixel architecture with DC coupling to the sensing 

diode and explore various diode sizes (from 1.2×1.2 µm 2 to 2.4×2.4 µm 2 ). The fourth sub-array exploits 

a more complicated pixel architecture (15 transistors instead of 8) with AC coupling to the sensing diode, 

which allows larger charge-to-voltage conversion gain. 

Preliminary tests exhibited noise levels in the order of ∼ 13-18 e − ENC, depending on the sub-array, and 

charge-to-voltage conversion gains of 50-70 (resp. 110) µV/e − for the different DC coupling (resp. AC 

coupling) architectures. Moreover, the pixel-to-pixel dispersion came out to be at an acceptable level. 

The chip is still being tested, and may be exposed to particle beams in Spring 2005 in order to assess 

its charged particle detection performances. Its architecture, which looks very promissing, provides the 

path to follow in 2005 for the next R&D steps towards a fast chip. The first step will allow optimising 

the MIMOSA-8 architecture in terms of noise, amplification, etc. Next, an ADC circuit will be integrated 

at the column ends, followed by the design of data sparsification micro-circuits. Meanwhile, the 

integrated architecture allowing to store and extract the cluster information is being developped. The 

choice between different possible signal processing architectures will be guided by the aim of keeping 

the power dissipation well below 1 W/cm 2 . 

The optimisation of the sensor performances will also depend on features specific to each fabrication 

process available (see part C) of this subsection). The fabrication process exploration will therefore be 

conducted in parallel and in close contact with the development of the sensor architecture.


Figure 2.6: MIMOSA-8 sub-array with AC-coupling to the sensing diode. schematic of the pixel architecture 

– principle of operation scheme representing the AC-coupling of the (auto-reverse polarised) 

charge sensitive element and the amplifier – sensor response to its illumination with an 55 Fe source. The 

peak around 180 ADC units is due to 5.9 keV X-rays impinging the sensor in the depleted volume surrounding the 

sensing diode, a case where the full X-ray signal charge (i.e. ∼ 1640 e − ) is collected by a single diode and can 

therefore be used for charge-to-voltage calibration purposes. 

B) Radiation tolerance 

The radiation tolerance of the sensors is a central issue of the R&D programme as the CBM running 

conditions are particularly harsh. CMOS sensor studies performed for their original application, i.e. 

light imaging, provide only little information or use for their application to charged particle tracking. 

The capability of the sensors adapted to tracking to stand high radiation levels requires therefore quite 

extensive investigations. 

Radiation tolerance studies were initiated in 2002-2003, mainly based on the first two MIMOSA prototypes, 

which provided preliminary estimates of the raw technology potential. Important steps were 

achieved in 2004 with more recent chips, essentially with ionising radiation, which allowed to make 

substantial progress both in the understanding of the origin of certain damages as well as in the way to 

improve the sensor tolerance. 

Bulk damage: The effect of bulk damage [46] was first investigated a few years ago, by exposing 

small prototypes (MIMOSA-1 and -2) to fluences of up to 10 13 neq·cm −2 . A decrease of the detection 

efficiency was observed, which started to have significant consequences on the detection efficiency for 

doses of about 10 12 neq·cm −2 . 

Similar neutron irradiations were repeated in 2004 with more recent prototypes: the reticle size sensor 

MIMOSA-5 and the prototype MIMOSA-9 designed to explore a new type of fabrication process (described 

in part C) of this subsection). The study of the irradiation effects on these chips is still to perform; 

it is foreseen to start early in 2005. 

Ionising radiation effects: The first estimates of the sensor tolerance to ionising radiation were achieved 

with MIMOSA-2, showing that an integrated dose exceeding ∼ 200 kRad was likely to affect the detection 

performances of this sensor. 

Further studies were performed in 2004, with integrated doses of up to 1 MRad [47]. They rely on 

tests of MIMOSA-2 and of another chip (called SUCCESSOR-1), borrowed from the SUCIMA collaboration 

[48] which designed it for imaging purposes. Both sensors were manufactured with the same


fabrication process, but featured different reset transistor designs: while the source of this (enclosed) 

transistor was in its center and the drain at its periphery in each MIMOSA-2 pixel, drain and source 

had swapped positions in SUCCESSOR-1, a feature expected to improve the chip tolerance to ionising 

radiation. 

The effect of ionising irradiation was tested by illuminating MIMOSA-2 and SUCCESSOR-1 chips, exposed 

to integrated doses of 400 kRad and 1 MRad, respectively, with an 55 Fe source. The response of the 

irradiated chips to the 5.9 keV X-rays emitted by the source is compared to that of non-irradiated chips on 

Fig. 2.7. The latter displays the distribution of part of the cluster charges in ADC units. While the distributions 

of the irradiated and non-irradiated SUCCESSOR-1 chips almost overlap, those of MIMOSA-2 

do not. The shift of the large peak observed for this sensor expresses a substantial charge loss due to 

radiation damage, which is not at all visible for the irradiated SUCCESSOR-1 chip, despite the 2.5 times 

larger dose it was exposed to. Though it is not yet fully established that the improved radiation tolerance 

is not due to some undocumented change in the fabrication process, there is strong support for the 

hypothesis that the source and drain swap is at its origin. 

Figure 2.7: Effect of 400 kRad exposure on MIMOSA-2 (left) and of 1 MRad exposure on SUCCESSOR-1 

(right). The response of non-irradiated (shaded histogrammes) and irradiated (black line) sensors is shown, when 

illuminated with an 55 Fe source. 

The radiation hardness of SUCCESSOR-1 was observed to be limited by the raise of the sensor’s sensing 

diode leakage current, translating into a hampering noise level. Cooling the sensor and shortening its 

integration time was found to be sufficient to dim this noise to a tolerable value for integrated doses of 

up to 1 MRad. These observations give a strong indication that the design of 1 MRad resistant CMOS 

sensors is under control. Moreover, they suggest that integrated doses well above 1 MRad may be 

tolerable. 

Since the present knowledge of the sensor radiation tolerance is still quite limited and needs to be better 

assessed, the coming years will largely be devoted to the necessary measurements. These encompass 

the estimate of the tolerance itself as well as of the electrical parameters governing the reaction of the 

sensors to intense radiation, e.g. I-V, C-V and, more generally, reverse engineering measurements within 

the limits allowed by the chip manufacturers. 

The sensitivity of the sensors may strongly depend on the fabrication process. Each of them needs therefore 

to be carefuly evaluated in terms of tolerance to bulk damage and ionising radiation effects. Clearly, 

priority will be given to manufacturing processes providing a feature size ≤ 0.35 µm. Transistors may be 

enclosed in order to protect them against polarity inversion induced by ionising radiation. However, this 

increases their size substantially, a feature which may come into conflict with the granularity required. 

Studies will therefore be pursued in order to eventually spot those transistors most exposed and active,


which could then be enclosed individually. 

The R&D effort will also touch the issue of procedures allowing to recover (at least partially) from damages 

due to radiation. Various treatments will be considered, which are mainly based on temperature and 

time considerations. The role of temperature needs actually to be investigated carefuly, and the behaviour 

of the sensors will be studied as a function of operating temperature. 

C) Exploration of fabrication processes 

Motivation and major issues: The capability of developing radiation tolerant and fast sensors depends 

on basic fabrication parameters such as the epitaxial layer, the feature size, the number of metal layers, 

the leakage current etc., which may vary substantially from one fabrication process to another. Finding 

well adapted fabrication processes, which allow both integrating the necessary signal conditionning 

micro-circuits in the sensor and optimising its design with respect to ionising radiation tolerance without 

degrading significantly the detection performances, is therefore a must. 

Several manufacturing processes have been explored up to now: 

• AMS-0.6 µm 

• AMS-0.35 µm (with and without epitaxial layer) 

• MIETEC-0.35 µm (which became AMI-0.35 µm recently) 

• TSMC-0.25 µm 

• IBM-0.25 µm 

Except of the IBM process (which exhibited a too thin epitaxial layer, presumably 2 µm), all of those 

processes allowed to realise prototypes with excellent detection performances. These very satisfactory 

results were obtained because the noise of the pixels could be kept to ∼ 10 e − ENC, a direct consequence 

of the simple pixel architecture which is free of signal treatment micro-circuits such as those necessary 

for fast parallel read-out. These micro-circuits are expected to increase the total noise value by a factor of 

2 to 3, restricting the choice of the manufacturing process to those exhibiting an epitaxial layer thickness 

8-10 µm. 

Some processes, relying on a lightly doped substrate but exhibiting no epitaxial layer, allow to circumvent 

this handicap, the substrate acting as an effective thick (i.e. several tens of microns) sensitive volume. 

This was demonstrated with two prototypes (called MIMOSA-4 and SUCCESSOR-2) studied in 2003. 

Their tests demonstrated an outstanding detection efficiency of up to 99.9 %, and a single point resolution 

of about 2.5 µm [49]. However, the cluster size happens to be rather large, especially at low operating 

temperature, a feature which reduces the double hit resolution of the sensor. Moreover, the absence of 

side effects when thinning the sensors to ∼ 50 µm needs to be demonstrated. 

Another concern is the number of metal layers, which is quite often equal to 3 or 4 only in standard 

processes. It deserves much attention once signal treatment micro-circuits are to be integrated in the 

sensor, imposing to chose processes offering at least 4 metal layers. 

Recent achievements: The manufacturer AMS announced a new fabrication process at the end of 

2003, with 0.35 µm feature size and about 20 µm epitaxial layer. This process was supposed to be


optimised for CMOS imaging applications, meaning that special care was taken of sources of leakage 

current in order to minimise the latter. MIMOSA-9 was designed and fabricated to explore this process. 

It is made of several sub-arrays, each exhibiting a different sensing diode or pitch size. The diode 

dimensions are either 3.4×4.3, 5×5 or 6×6 µm 2 . The pitch is either 20, 30 or 40 µm in both directions. 

Several prototypes were exposed to a ∼ 120 GeV/c pion beam at the CERN SPS. Excellent performances 

were observed [50], as illustrated by Fig 2.8. The signal-to-noise most probable value ranges from ∼ 20 

to 30, depending on the diode and pitch. This rather comfortable magnitude translates into a detection 

efficiency exceeding 99.5 %, even in the case of a pitch as large as 40 µm, where charge collection may 

show some inefficiencies due to the sizeable path achieved by some charge carriers until the sensing 

diode. The single point resolution was also found to be excellent ( ∼ 1.5 µm for a 20 µm pitch). 

Seed pixel noise for real track cluster 

2500 

2000 

1500 

1000 

500 

0 

hRTN 

Entries 6067 

Mean 8.665 

RMS 1.247 

6 8 10 12 14 16 18 20 22 24 

Electrons 

Events 

Signal/noise in 1 pixels 

180 

160 

140 

120 

100 

80 

60 

40 

20 

hsn1 

Entries 6067 

Mean 41.07 

RMS 23.57 

Underflow 0 

Overflow 202 

2 

χ / ndf 199.8 / 131 

Constant 930.5 ± 18.14 

MPV 26.27 ± 0.188 

Sigma 6.521 ± 0.1017 

0 

0 20 40 60 80 100 120 140 

Signal/Noise 

Figure 2.8: MIMOSA-9 beam tests results at 0 ◦ C for pixels of 20 µm pitch hosting a 3.4x4.3 µm 2 diode.The 

distribution on the left stands for the residual noise while the pixel signal-to-noise is displayed on the right side of 

the figure. 

Overall, these results make this fabrication process most attractive. There are however two features 

which weaken this statement. One of them concerns the total cluster charge, which was found to be 

around 900 e − ENC. This amount corresponds to an epitaxial layer of ∼ 11 µm, a thickness which was 

confirmed by visual check with a microscope, thus infirming the 20 µm announced by the founder. The 

second source of concern is related to the leakage current. The latter was indeed measured to be about 

one order of magnitude below the typical values of previous chips, but it increased by close to two orders 

of magnitude after 20 kRad irradiation with a hard X-Ray source. This increase, which raised the noise 

by more than a factor 2, was however not observed for all sub-structures integrated in the chip, hinting to 

a possibility to contain the current increase. In conclusion, despite the weaknesses of this new fabrication 

process, it can still be considered as providing better performances than any of the other processes with 

epitaxy used up to now. 

The R&D goals of the next years will include testing fabrication processes offering a feature size below 

0.25 µm, representative of the trend of the CMOS industry. Processes envisaged encompass IBM-0.13 

µm, UMC-0.18 µm (with triple well option) and AMS-0.18 µm (with low leakage current option). 

D) Thinning 

General remarks: The question of thinning was not much addressed in the first years of the sensor 

R&D as it was not an issue of prime importance at that time. Ultimately, it will however become so since 

the single point resolution offered by the sensors sets a severe constraint on the material budget if one 

aims to avoid swamping the resolution with multiple scattering. 

The thickness ambitionned is typically 50 µm and, if possible, twice less. While thinning sensors to ∼ 50


µm is not expected to be particularly difficult or suspected to affect the sensor performances, a thickness 

of about 20 µm is considered as a challenge. The ability of industry to achieve it with a satisfactory 

yield needs to be assessed. Moreover, internal mechanical stress may occur, translating into wrinkling 

of the chip. The answer to this side effect may consist in glueing the chip on a thin carbon foam or 

beryllium support, which would ensure the necessary rigidity. A dedicated research effort is needed, 

which oughts to encompass bonding issues, to investigate such possibilities. It may profit from similar 

studies performed for the RHIC upgrades and for the Linear Collider project. 

Achieved thickness: It is now established since a few years that reticle size chips (i.e. MIMOSA-5) 

can be thinned down by industry to 120-130 µm, without any noticeable side effect. This is still 2 or 3 

times too much for the resolution ambitioned on the track origin. Attempts were made in 2003 and 2004 

to thin MIMOSA-5 sensors down to ∼ 50 µm or even much less. 

The most agressive thinning trials were performed for imaging purposes by the SUCIMA collaboration 

(E.U. 5th Framework Programme). The substrate was totally removed with a non-standard method, leaving 

the epitaxial layer protected by only a few hundreds (or even tens) of nanometers of thin material. 

This procedure, which resulted in a ∼ 15 µm thin sensor, allowed to make it sensitive to electrons of a 

few keV only, as demanded for beta-imaging purposes. These sensors were exposed to ∼ 120 GeV pion 

beams at the CERN-SPS in order to assess their minimum ionising particle detection performances. The 

detection efficiency was observed to amount only to ∼ 85 %, due to a substantial drop of the charge 

collected. This thinning method is thus not (yet) adapted to minimum ionising particle detection. Improvements 

are however not excluded. 

Another, less agressive, thinning attempt was made, guided by the development for the STAR vertex 

detector upgrade. MIMOSA-5 wafers were thinned down to ∼ 55 µm via LBNL collaborators with 

rather conventional industrial means. The first wafer thinned in this way (Autumn 2004) exhibited cracks 

shortly after thinning, of which the origin is still being investigated. Meanwhile, the procedure was 

repeated with individual sensors after their dicing. The thinned sensors were bonded to a mezzanine 

DAS board using a 50 µm thin film adhesive at LBNL. Preliminary test results show that this bonding 

technique is efficient and that the sensor functionalities tested are all preserved. 

In conclusion, the procedure allowing to thin down the sensors to ∼ 50 µm is still not fully established, 

but the first test results are very promissing. 

2.2.3.5 Plans for 2005-2007 

A) Fast read-out 

In order to minimize the costs of prototype fabrications, the latter will be of minimal size (typically 5 – 

20 mm 2 ) and manufactured within multiproject runs. However, since the functionalities to integrate on 

the sensor require a large number of transistors (i.e. several tens of millions) integrated on each chip, the 

fabrication yield will be an issue. Its assessment requires fabricating medium or reticle size sensors as 

well, which may cost up to 200 k. 

Plans of prototype fabrication in the coming three years look as follows: 

2005: fabrication of a small prototype (TSMC-0.25 µm multiproject run) optimising the fast architecture 

of MIMOSA-8 (see A) in subsection 2.2.3.4) and hosting the first ADC test structures. The 

expected cost is ∼ 20 k.


2006: fabrication of a small prototype (TSMC-0.25 µmmultiproject run) extending the architecture above 

by including an ADC at the end of each column. The expected cost is ∼ 20 k. 

2007: fabrication of a macroscopic prototype (engineering run) allowing to test on full scale the fast and 

radiation tolerant archtitecture with pre-amplification and CDS inside each pixel and ADC at the 

end fo each column. The cost may be in the range 150–200 k. 

B) Radiation tolerance 

Prototyping is mainly needed for improving the tolerance to ionising radiation. For the two coming years, 

dedicated prototypes will be fabricated for this issue. The outcome of this research phase should lead to 

a structure to be integrated in the fast prototype fabrication scheduled for 2007 mentioned above. 

The plans for 2005-2007 are thus as follows: 

2005: fabrication of a small prototype (AMS-0.35 µm OPTO multiproject run) exploring various structures 

expected to improve the tolerance to ionising radiation. The expected cost is ∼ 10 k. 

2006: fabrication of a small prototype (multiproject run) improving and extending the structures fabricated 

in 2005. The expected cost is ∼ 10 k. 

2007: integration of the architectures developed in 2005 and 2006 in the macroscopic fast chip mentioned 

in part A) of the present subsection. 

Besides these applications, the study of the sensor sensitivity to bulk effects will continue, based on the 

different chips fabricated in 2004-2007 and exposed to ∼ 1 MeV neutrons in Dubna. 

C) Fabrication processes 

Several CMOS manufacturing processes based on feature sizes smaller than 0.25 µm will be explored, 

typically one per year. The processes presently offered by industry and potentialy of interest, are IBM- 

0.13 µm, UMC-0.18 µm (triple well) and the announced AMS-0.18 µm (OPTO) process. The expected 

cost of each prototype is ∼ 5 k. 

D) Thinning 

A thickness of ∼ 50 µmseems within reach, based on the development going on at LBNL with MIMOSA- 

5 chips for the STAR upgrade. In order to minimise the dependence on a single industrial supplier, it 

may be worth finding one or two additional providers. Trying to thin the sensors below 50 µm will be 

the next step. Another objective will consist in thinning sensors fabricated without epitaxial layer but 

featuring a low doping substrate as sensitive volume. 

The plans for 2005–2006 look presently as follows: 

2005: thinning studies currently performed with MIMOSA-5 chips for the STAR upgrade at LBNL will 

continue. Other thinning possibilites, relying on industrial or academical possibilities in Europe, 

will also be tried, still relying on MIMOSA-5 sensors. The stock of the latter may however get 

exhausted rapidly if thinning needs to be tried on wafers rather than on individual sensors. Fabricating 

extra MIMOSA-5 wafers may therefore become necessary. The cost is expected to be ∼ 50 

k.


2006: trials may be made to thin MIMOSA-5 sensors below 50 µmand/or to thin sensors without epitaxial 

layers. Depending on the exact operations to be done, the cost may go up to ∼ 50 k. 

E) Final fabrication 

The fabrication cost of the final sensors will depend on parameters which are still to be determined via 

detector performance studies (total surface to cover, pixel pitch, sensor thickness, etc.) or to come out 

from the sensor R&D (which is aimed at finding a fabrication process offering a satisfactory trade off 

between a small feature size, a thick sensitive volume, etc. and an affordable price ). 

A first guess suggests that the final sensors will be fabricated via 1 or 2 engineering runs in a very 

deep sub-micronic process (such as IBM-0.13 µm), each costing ∼ 200 k. This estimate is based on a 

fabrication yield of 40–50 %. It does not account for the (large) spare lot needed if one aims to replace 

the sensors affected by radiation effects in case of high beam intensity running conditions. 

2.2.3.6 Framework of the R&D 

CMOS sensors are being developed in Strasbourg since 1999 in perspective of various applications, 

which range from vertex detectors for subatomic physics (e.g. STAR upgrade, Linear Collider experiment) 

to bio-medical imaging (e.g. beam monitoring for oncotherapy) and operational dosimetry (e.g. 

control of ambient radon and neutron radiation levels in nuclear plants). 

Several application domains call for SoCs providing fast read-out speed, high radiation tolerance, minimal 

material budget and low power dissipation. Developments for the CBM experiment will thus benefit 

from the synergy with the R&D aiming for other applications, in terms of fabrication process exploration, 

development of fast signal processing architectures, radiation tolerance investigations and improvements, 

thinning procedures, etc. More information on the activities and achievements of the Strasbourg research 

team is available in [51]. 

2.2.4 Alternative technologies 

Monolithic pixel sensors are certainly the detectors of choice for the CBM ITS once the technological 

problems still existing today are solved. Besides several design issues which will require only moderate 

R&D, the main progress has to be made with respect to radiation tolerance and read-out speed. Only 

with this type of detector integral material budget per active layer substantially below 0.5 % are in reach. 

The R&D efforts to overcome these drawbacks can not be carried out by the CBM collaboration but 

rather strong cooperation with groups working in detector development for future high energy physics is 

needed. As a fall back solution in case the progress in R&D will fall behind the projected time schedule 

for construction of the CBM experiment, we envisage to use hybrid technology. The larger material 

budget will have some impact on the physics performance, but not in all cases high vertex resolution 

is of high importance. In the following, we present a scenario assuming hybrid pixel, which is used 

to conduct detailed performance study using realistic detector geometries. An alternative technology 

aiming at monolithic sensors is presented in the subsequent section. 

2.2.4.1 Hybrid pixel detectors 

Present hybrid pixel tracking detectors fulfil most of the performance parameters required for operation 

in the CBM experiment. The major drawback is the high material budget and, to some extend, the minimum 

pixel size achievable, which itself is dictated by the area needed to place the electric components


necessary to read out a pixel. Compared to the monolithic pixel detector, the main difference here is the 

fact that the read-out chip is produced in standard CMOS technology and thus not limited in the design 

complexity. The MAPS read-out structures in contrast are implemented on the sensor substrate and are 

thus restricted to NMOS transistors [52]. 

The pixel read-out chips developed for LHC experiments in general provide fast enough read-out speed. 

The devices are triggered by the bunch crossing which appear at a rate of 40 MHz. Pixels which show an 

integrated charge above a programmable threshold generate their address number and a number proportional 

to the charge on the pixel. The latter is a time-above-threshold (ATLAS) or the integrated current 

pulse (CMS). It was shown by the CMS collaboration that a digitization of 3 bit resolution is sufficient 

to reach a position resolution substantially smaller than the pixel size divided by √ 12. 

In a broad R&D effort by the RD42 collaboration it was shown that pixel sensor material can be produced 

with sufficient radiation hardness. The major breakthrough came from the discovery, that oxygenated 

silicon is radiation hard against damage caused by non-ionizing energy loss due to protons or pions [53]. 

The read-out chips are produced in radiation hard DSM technology and show stable operation up to 

fluency of 10 15 neq. 

The material budget used for detector infrastructure, i.e. support, cooling and connectivity, amounts 

to typically 50 % of the total material budget. The read-out chip are usually thinned down after being 

bonded to the sensor chip. Substantial progress in optimizing this operation was achieved in recent years. 

The thickness of the sensor cannot be reduced beyond presently below current values without loosing 

to much signal. The sensor thickness also influences the degree of charge sharing between neighboring 

pixels. A compilation of performance parameters of some pixel detectors systems in high energy physics 

is given in Tab. 2.2. 

Experiment Pixel geometry Sensor Chip Infrastructure Total 

µm 2 µm µm X/X0 % X/X0 % 

ALICE SPD 50 × 400 200 150 0.44 0.81 

CMS 100 × 150 285 175 0.51 1.0 

ATLAS 50 × 400 280 150 0.16 0.7 

BTEV 50 × 400 250 200 0.77 1.25 

ITS 100 × 140 200 150 0.13 0.5 

Table 2.2: Parameters of pixel tracking stations to be used in upcoming high energy experiments. 

Conceptional design of a ITS with hybrid pixels. 

As a standard detector module we assume a sensor geometry of 2 × 5 cm 2 which will be read-out by 

five read-out chips bonded to the sensor. A read-out chip is assumed to have an active cell size of 

100×140 µm 2 with the cells arranged in 100 rows and 140 columns (edge effects due to inactive regions 

at the chip borders are neglected at this stage of the simulation). A single chip would thus cover 14.000 

pixels. We assume a modified geometry with six read-out chips for the modules being placed close to 

the beam axis. The layout of the two types of modules is depicted in Fig. 2.9 

Each module would be installed on a support structure made of carbon substrate which would also 

provide cooling. The assumed material budgets are extrapolated from the numbers listed in table 2.2 

assuming moderate progress in the material budget for the detector infrastructure since lower power consumption 

due to 0.13 µm technology could reduce the cooling efforts. In addition we take the advantage 

to place the connecting bus outside the active area of the innermost modules.


Figure 2.9: Conceptional design of the hybrid pixel modules used for simulation. 

2.2.4.2 SOI sensors for the PVL 

The MIMOSA chip relies on bulk CMOS technologies and thermal diffusion of the charge generated 

in a lightly doped epitaxial layer towards the collecting N-wells. In such a solution, however, due to 

the small detector active volume the charge generated by a minimum ionising particle is small (of order 

of few hundreds up to 1000 electrons) and only NMOS circuitry may be implemented in the front-end 

electronics. The kind of the sensors that does not suffer from these limitations is a monolithic active 

pixel sensor realized in Silicon on Insulator technology (SOI sensors). The concept of this device relies 

on the utilization of both silicon layers (support and device layers) of the SOI substrate for the realization 

of the pixel detector diodes and the readout electronics respectively. Fabrication of a monolithic active 

pixel detector in the SOI substrate may potentially not only lead to good detection efficiency and short 

charge collection times, but also to improved circuit immunity for single event radiation effects (SEE) 

and to increased design flexibility due to the possibility of use of transistors of both types in the readout 

channel. 

In contrast to the CMOS sensors, the manufacture of the SOI sensors requires a dedicated non-standard 

technology, which results in higher costs and longer production times at the early stages of the development. 

Nevertheless, once the technology is defined these problems can be overcome and the stability 

and continuity of the production is guarantied. It is also worth to point out that the growing interest of 

the semiconductor industry in the SOI sensors development has been observed recently which may lead 

to the easier access to the advanced submicron technologies for the sensor manufacturing and thus to the 

improved performance of the future prototypes. 

The development of the SOI sensors was started just few years ago, but detection efficiency of such 

detectors has been already demonstrated in series of tests. The radiation tolerance and the fast read-out 

operation must be however still investigated and will be the key issues of the future development. 

Main features and R&D status of the SOI sensors. 

The operation of the SOI sensors relies on the detection of the ionising radiation in the high resistivity 

support layer of the SOI wafer and the signal processing in the readout electronics created in the device 

layer of the same wafer [55]. The cross-section of such device is presented in figure 2.10. As it is 

presented, the charge sensitive part of the sensor has a conventional form of reversed biased p+-n junctions. 

The collecting diodes are monolithically coupled with the read-out electronics by a connection 

that passes through the ultra-thin silicon film and the buried oxide (BOX). The attractive feature of the 

SOI sensor is the separation of the detector and electronics active volumes. It allows the integration of


Figure 2.10: Integration of particle sensor and readout electronics in the SOI substrate. 

the advanced signal processing (amplification, pedestal correction, filtering, discrimination, digitisation, 

etc.) directly in the readout cell using standard solutions of the Complementary MOS circuit theory. 

The key feature of the developed SOI sensor is the exploitation of the wafer-bonded SOI substrates 

for the integration of the particle sensor and readout electronics in one entity. Such approach allows the 

optimisation of the resistivity of the detector and electronics substrates. The further important advantages 

of the wafer-bonded substrates in comparison with other popular SOI substrates obtained in the SIMOX 

process are also lower level of the structural defects in both the device layer and the handle wafer, absence 

of silicon inclusions and islands in the buried oxide (BOX), and lower temperature processing. All of 

those characteristics of the wafer-bonded substrates are of high importance from the detector quality 

point of view. 

The concept of the SOI sensor has been already validated by the successful production and tests of 

several iterations of small area test detectors. Recently a larger, fully functional prototype of the SOI 

detector, which consists of more than 16 thousands channels and covers the active area of 4 cm 2 , was 

also produced and is currently under investigation. The photo of the 4-inch wafer of SOI detector test 

structures and the photo of the new large area prototype are shown in figure 2.11. 

Future development for the CBM experiment. 

The development of the SOI sensor for the CBM experiment requires addressing such important issues 

as device thinning, development of the fast readout electronics and radiation hardness. These problems 

are the crucial elements of the R&D framework for the next years and will be shortly described in the 

following. 

Thinning issues: In contrast to the CMOS sensors, where the amount of the charge generated by an 

ionising particle is determined by the technology parameters, in the SOI sensors the charge signal is 

proportional to the sensor thickness. For this reason the sensor thickness should be optimised for the 

particular application and must be a result of the compromise between the acceptable materials budged 

of the tracking system and the desired signal to noise ratio. 

For the SOI sensors the same thinning technologies as for the hybrid or CMOS sensors may be applied. 

Thinning down to 50 µm is already within the reach of existing technologies and should not be a problem. 

Together with the progress in the field of the thinning techniques the sensor thickness reduction below 

50 µm can be also considered. In such case however the thinning side effects, such as mechanical stress 

will have to be assessed.


Figure 2.11: The photo of the wafer with SOI detector test structures (left) and the photo of a first fully functional 

SOI sensor prototype, covering active area of 4 cm 2 and consisting of about 16.000 readout channels (right) . 

When the SOI sensor thinning is discussed it should be also emphasised that almost the whole SOI 

sensor thickness is the detector active volume. The total thickness of the electronics and buried oxide 

layers does not exceed 2.2 µm (can be reduced to several hundreds of nanometres) and thus only slightly 

contribute to the material budged of the tracking system. 

Radiation tolerance studies: The radiation tolerance study of the SOI sensor is a complex problem. It 

has to take into consideration the radiation damages of several regions of the sensors: the electronics 

part, the detector active volume and the buried oxide. For each of the regions different radiation damage 

mechanism will be dominating and different radiation hardening method will have to be applied. It 

might be expected that in case of the SOI sensor, which characterizes by the implementation of the 

readout electronics in relatively thin silicon film, the problem of the front-end electronics immunity 

to the single event effects caused by high energy particles will be of lower importance. Nevertheless 

the total ionisation dose effects and the total displacement effects will still have to be assessed. In the 

terms of sensor resistance to the detector bulk damages it is possible that some experience gained during 

the development of the radiation hard detectors for the LHC experiments might be adopted in the SOI 

sensors. In particular the wafer bonding technique does not exclude the usage of such detector materials 

as the diffusion oxygenated float-zone silicon or the high resistivity Czochralski silicon. In the hitherto 

development however only the classical FZ silicon substrates have been exploited. As long as the sensor 

sensitivity for the total ionisation dose is discussed the radiation damages of the gate, field and buried 

oxides have to be considered. The influence of the charge trapping in the field oxide on the circuit 

performance may be probably effectively reduced by the proper design techniques, but the gate oxide 

damages even in the case of the 0.6 µm technology may still play an important role and will have to be 

carefully studied. Even more attention should be paid to the investigations of the results of the BOX 

radiation damages, since this problem is a peculiarity of the SOI sensor. 

The radiation tolerance of the sensor has not been addressed so far and will have to become one of the 

main tasks of the future development. The total dose effects will be studied with the 10 keV X-ray 

generator in Karlsruhe or at CERN and the bulk damages will be investigated in the tests with a neutron 

beam. The extensive radiation hardness experiments will be accomplished by the 3-D device simulations 

performed with the ISE-TCAD package, which will allow examining the main origins of the radiation 

sensitivities of the SOI detectors.


Development of the fast readout: The simplest method of the fast readout development is the segmentation 

of the detector and the parallel readout. Such technique has been already implemented in the recent 

SOI sensor prototype where the total detector matrix was divided into 4 sub-matrices with 64 by 64 readout 

channels each. Such method in the case of the large detection systems leads however to the dramatic 

increases of the data flow. For this reason more advanced techniques, relying on the preliminary data 

reduction in the readout electronics will have to be applied. 

In case of SOI sensors the implementation of the signal pre-processing in the readout cell seems to be a 

simpler task than in the CMOS sensors, since the SOI approach allows the usage of both transistor types 

(PMOS and NMOS) in the readout cell. The integration of the advanced electronics directly in the readout 

channel is still restricted by the limited access to the deep submicron technologies for the SOI sensor 

production and the maximum available area for a single channel of the pixel detector. The reasonable 

solution may be integrating basic signal processing (correlated double sampling, discrimination, etc.) in 

the readout cell and exporting more complicated operations (signal digitisation and data sparsification) 

to the readout circuit peripheries. 

Framework of the R&D. 

The SOI sensors are being developed in the collaboration between Institute of Electron Technology in 

Warsaw and AGH-University of Science and Technology in Krakow since 2001. The work on the new 

sensor generation are mainly motivated by demands of the medical applications (beam monitoring in 

hadrontherapy, dosimetry of radioactive source for brachytherapy) and requirements of future nuclear 

physics experiments. The main research goal is the development of a pixel detector that would be free of 

such limitation of the existing technologies, like the high material budget or low signal levels generated 

by the ionising particles. The R&D framework of the SOI sensor is thus in agreement with the demands 

of the CBM experiments. More information about achievements of the Krakow research group may be 

found on the webpage of the SUCIMA project [48]. 

2.3 The Silicon Strip Tracker (SST) 

2.3.1 General design considerations 

The challenge for the CBM Silicon Strip Tracker (SST) is to measure with high precision and efficiency 

the trajectories of up to 1000 charged particles per event at event rates of up to 10 MHz. The design of 

the CBM Si-Strip Tracker is determined by the following requirements: 

• high granularity to keep the occupancy below a few percent 

• high position resolution for momentum determination and for extrapolation of the measured tracks 

towards the Silicon Pixel vertex detectors 

• low material budget to reduce multiple scattering 

• large acceptance, i.e. angular coverage from 50 to 500 mrad corresponding to the acceptance of 

the spectrometer 

• stand-alone pattern recognition 

• time resolution sufficient to provide the event time stamp required for a data driven FEE and DAQ 

architecture

2.3. The Silicon Strip Tracker (SST) 45 

• operation in a harsh radiation environment. 

• cost effective detector layout 

The HEP community has great experience in the design and development Si-strip sensors and precision 

Si-strip tracking systems. For example, Si-strip vertex detectors were successfully operated in a fixed 

target configuration in HERA-B at DESY [56], and are proposed for the VELO detector [57] and 

the Inner Tracker [58], both part of the LHCb experiment at CERN. Similarly, the Si-strip STS for 

CBM exhibits a forward spectrometer configuration: The detectors planes are mounted in subsequent 

layers perpendicular to the beam axis at appropriate distances to provide optimal conditions for pattern 

recognition and momentum measurement in a magnetic field. The total length of the tracking system 

(including the Pixel vertex detector) is about 1 m in order to fit into the spectrometer magnet. The 

general layout of the CBM Si-Strip STS is presented in figure 2.12. 

4 Si−STS stations 

cross section at x=0: 

X 

STS4 

0 10 20 cm 

STS5 

STS6 

STS7 

500 mrad 

50 mrad 

Figure 2.12: Arrangement of the Si-strip layers along the beam axis. The dashed lines indicate the maximum 

and minimum angular coverage of the Si-Strip STS 

The actual layout of the Si-Strip Tracker features 4 layers with a relative distance of 20 cm. The number 

of layers, however, as well as their positions are subject of further optimization based on simulations 

of track reconstruction in the inhomogeneous magnetic field. The first and the last layer are located at 

distances of 40 cm and 100 cm downstream from the target (z 0 cm). The minimum distance ri from 

the active detector area to the beam axis is 2 cm at z 40 cm and 5 cm at z 100 cm. The positions and 

the inner and outer radii of the layers are listed in Table 2.3. As indicated in Fig. 2.12, all tracks with 

angles between 50 mrad ≤ θ ≤ 500 mrad will cross the 4 Si-strip layers. The number of measured space 

points will be sufficient to reconstruct the tracks in the magnetic field with the required efficiency and 

resolution. 

Z


2.3.1.1 Low-mass double-sided Si-strip Sensors 

Number Position ri ro 

[mm] [mm] [mm] 

1 40 20 200 

2 60 30 300 

3 80 40 400 

4 100 50 500 

Table 2.3: Positions of Si-strip planes along z-axis. 

Modern Silicon technologies achieve strip pitch values as small as 25 µm resulting in a coordinate resolution 

of about 8 µm. In this case the precision of track reconstruction is limited mainly by multiple 

scattering in the material of the silicon detectors. In order to benefit from the excellent intrinsic position 

resolution of the Si-strip detectors we propose to use very thin (100 µm) double-sided Si micro-strip sensors 

for the CBM tracking system. Such thin detectors are highly desirable since many relevant quantities 

scale favorably with decreasing thickness w [59]: 

• multiple scattering contribution ∝ √ w , 

• leakage current ∝ w , 

• full depletion voltage ∝ w 2 , and 

• power dissipated in sensitive area ∝ w 3 . 

On the other hand, a reduction of the detector thickness implies also a reduction of the signal-to-noise 

ratio (S/N) depending on the relative contributions from series (ENCS) and parallel (ENCP) noise: 

rL · rC · S280 · (w/280µm) 

S/N = 

ENC2 S + ENC2 P280 · (w/280µm) 

where S280 denotes the most probable charge deposition (23300 electrons) of a minimum ionizing particle 

in a 280 µm thick silicon detector, and rL and rC account for Landau fluctuations and charge collection 

deficits after exposure to high particle fluxes. 

Fortunately, the beneficial effects of a reduced detector thickness scale, in general, with a higher power 

of w than the loss in S/N. Fig. 2.13 depicts the signal-to-noise ratio according to eq. 2.1 assuming a 

quite strong signal reduction (rL = 0.67 and rC = 0.9). For the indicated realistic values for series and 

parallel noise a sensor as thin as 150 µm is expected to yield still an acceptable signal-to-noise ratio of 

almost 8. Our goal is to reach a Si-sensor thickness of 100 µm. Another advantage of such a thin detector 

is that the corresponding full depletion voltage would be a factor of 2 lower as compared to a 300 µm 

detector. If detectors as thin as 100-150 µm would become available, the Si-strip STS lifetime thus could 

be significantly extended, practically without loss of performance. 

The choice of single or double sided sensors for CBM is still an open issue. Clear arguments in favor of 

using double sided sensors are : 

• savings in radiation length of up to 50 % per sensor layer, 

• intrinsic high precision alignment of front and back strip diodes, 

(2.1)


Figure 2.13: Signal-to-noise ratio S/N as a function of detector thickness for indicated equivalent noise charges 

of series and parallel noise ENCS/ENCP280 in electrons (see eq. 2.1). The parallel noise value ENCP280 applies 

to a 280 µm thick detector. 

• up to 50 % reduction of heat generation from leakage current. 

On the other hand, unique advantages of single sided detectors are: 

• operation at partial depletion, 

• non-floating readout electronics, 

• availability of ≤ 280 µm n-type wafer material. 

Obviously, the final decision has to await further clarifications, in particular, as other criteria like final 

costs, yield, reliability, and, last but not least, radiation hardness have to be taken into consideration. 

Generally, double-sided Si-strip sensors are more suitable for the CBM conditions, and we hope to 

benefit from the impressive development of Si-strip sensor technology. 

2.3.1.2 Long Ladder Si-strip Sensor Technology 

Further optimization of the material budget may be achieved with the modern long ladder technology for 

the production of Si-strip detectors, which exceeded the maximum size of the commercially available 

Si wafers. This technology provides the possibility to significantly reduce material and elements inside 

the sensitive area of the Si-strip STS, in particular the front-end electronics and the number of readout 

electronics channels. 

The number of readout channels is defined by the readout pitch of 25 µm and the detector dimensions. In 

the final sensor layout the readout pitch can be slightly readjusted such that the numbers of stereo strips 

on a wafer are integer multiples (1/2 or 1/4), i.e. the standard number of channels integrated on a readout 

chip. A power consumption of a few mW is assumed per readout channel. For the output line drivers it 

should be considerable less. 

2.3.2 Geometry of the Si-strip tracker 

Simulations based on GEANT4 within the CBM virtual Monte Carlo framework were performed in 

order to optimize the layout of the Si-strip tracker. The realistic geometry of the detector is shown in


fig. 2.14 as it is implemented in GEANT4. In this layout, the first three (vertex tracker) planes of the STS 

are hidden inside the conical beam pipe, and the four layers of the Si-strip tracker are visible as detector 

planes. For better visualization the yoke of the magnet has been removed, and only the magnet pole shoes 

are shown. In order to optimize the performance of the Si-Strip tracker for high track densities, central 

Au+Au collisions at 25 AGeV as generated by the UrQMD code have been studied. The corresponding 

radial occupancies along the x axis in the central parts for the 4 planes of Si-strip tracker are presented 

in figure 2.15. The highest hit density is expected for the inner part of the first Si-Strip tracker plane 

(STS4). Given the radial distributions of charged particle densities as shown in figure 2.15, the size of 

the Si-Strip sensors can be defined: 

• strip pitch of 25 µm 

• occupancy of 2.5 %. 

• strip length of 2 cm in regions close to the beam 

Figure 2.14: The geometry of Si-Strip STS in GEANT 4 CBM Virtual MC 

In order to reconstruct the charged particle tracks efficiently and to obtain a momentum resolution in 

the order of 1%, the space resolution of the Si-strip detector should be in the order of ≈50 µm in both 

coordinates. The two-dimensional position sensitivity will be provided by the double-sided stereo angle 

topology of Si-strip sensors. Using this his method one has to find a compromise between the maximal 

possible space point resolution and the fake rate which is a feature of stereo strip topology of sensors. The 

performance of the different design options has been studied in simulations based on GEANT4 Virtual 

Monte Carlo, applying algorithms of hit production with strip structures close to the real design of double 

side Si-strip sensors. A sketch of the Si-strip sensor geometry is shown in figure 2.16 together with the 

hit definition. In the first step of the analysis we did not include the mechanism of charge splitting 

between neighbored strips as illustrated in figure 2.16. The space point resolution can be estimated from 

the Si-strip sensor topology. The resolution given by the pitch (coordinate 1) is 

σ1 = ps 

√12 

(2.2)


/event 

2 

Hits/cm 

/event 

2 

Hits/cm 

STS 4, z = 0.4(m) 

6 

5 

4 

3 

2 

1 

0 

0 2 4 6 8 10 12 14 16 18 20 

x [cm] 

STS 6, z = 0.8(m) 

1.6 

1.4 

1.2 

1 

0.8 

0.6 

0.4 

0.2 

0 

0 5 10 15 20 25 30 35 40 

x [cm] 

/event 

2 

Hits/cm 

/event 

2 

Hits/cm 

STS 5, z = 0.6(m) 

2.5 

2 

1.5 

1 

0.5 

0 

0 5 10 15 20 25 30 

x [cm] 

STS 7, z = 1(m) 

1 

0.8 

0.6 

0.4 

0.2 

0 

0 5 10 15 20 25 30 35 40 45 50 

x [cm] 

Figure 2.15: Charged particles occupancy in the four Si-strip planes as a function of the horizontal distance (in 

cm) from the beam centre. 

with ps the pitch of the Si-strip sensors. 

For a Si-strip pitch size of 25 µm the resolution is σ1 = 7.2 µm. The resolution in the perpendicular 

coordinate 2 is given by: 

σ2 = 2 × (ps) × 0.8 

√ 12 ×tg(θ) 

with 0.8 the correction factor for the non uniform distribution of the cross section along the coordinate 2 

and θ the stereo angle. For a stereo angle of 15 ◦ and a pitch size of 25 µm the resulting resolution is σ2 = 

46 µm. The analysis of charge division between neighbouring strips will significantly improve the space 

point hits resolution. 

The number of fake hits is given by: 

with n the number of real hits in the area of overlapping stereo strips. 

(2.3) 

Nf ake = n × (n − 1) (2.4) 

For particle occupancies corresponding to central Au+Au collisions and a stereo angle of 15 ◦ the expected 

number of fake hits in the innermost parts of the Si-strip plane STS4 amounts up to 80% of all 

hits. This large number of fake hits is a big challenge for the track finding algorithms. In the outer 

parts of the detector the number of fake hits drops dramatically and will not deteriorate the track finding 

procedure. Figure 2.17 illustrates the hit distributions including fake hits in the 4 Si-strip planes for one 

event of a central Au+Au collision at 25 AGeV.


Strip 

j−1 j j+1 

i−1 

000000 

111111 

000000 

111111 

000000 

111111 

000000 

111111 

000000 

111111 

000000 

111111 

000000 

111111 

000000 

111111 

000000 

111111 

000000 

111111 

000000 

111111 

000000 

111111 

000000 

111111 

000000 

111111 

000000 

111111 

000000 

111111 

000000 

111111 

000000 

111111 

000000 

111111 

000000 

111111 

S 

STS 

Figure 2.16: the Si-Strip STS sensors layout (principal idea and hit definition) 

2.3.2.1 Design of the Si-strip Planes 

A detector element consisting of double-sided sensors with a sensitive area of 60 × 60 mm 2 can be cut 

from a 4 inch wafer. In the future it is planned to apply the 6 inch wafers technologies. Figure 2.18 

displays the layout of the 4th and 6th layer of the Si-strip STS which consist of Si-strip sensor elements 

of different sizes. The expected particle density is highest at the 4th Si-strip layer which, therefore, 

has to exhibit the highest granularity. The intermediate zone of the 4th layer consists of single Si-strip 

sensors with longer strip length according the required occupancy. The outer zone of the Si-strip plane 

is composed of Si-strip modules built in Long Ladder technology. In the 4th Si-strip layer the ladder 

consists only of two Si-strip sensors.


Figure 2.17: Hit distribution on the STS planes 4, 5 and 6 including fake hits for one event of central Au+Au 

collision at 25 AGev 

STS4 

STS6 

000000 

111111 

000000 

111111 

000000 

111111 

01 

00 11 000000 

111111 

01 

00 11 000000 

111111 

01 

00 11 

01 

00 11 

01 

00 11 

01 

Y 

000000000 

111111111 

000000000 

111111111 

000 111 

000000000 

111111111 

000 111 

000000000 

111111111 

000 111000 

111 

000000000 

111111111 

000 111000 

111 

000000000 

111111111 

000 111000 

111 

000 111 

111 000 

111 000 

X 

0 10 20 cm 

Figure 2.18: the Layout of the Si-Strip STS4 and STS6 Planes


2.3.3 Si-strip sensors: Design and Technology 

The ultimate goal is to develop radiation-hard n-type double-sided capacitively coupled silicon strip 

detectors with a thickness of 100 µm. The aim is also to simultaneously simplify the design and the 

production of the detector in order to reduce costs [60]. The preferred detector concept is depicted in 

figure 2.19 illustrating the design of a n-type double-sided strip detector [56]. Double-sided micro strip 

sensors are built from 100 mm diameter wafers of n-type material with a resistivity of 5-7 kOhm cm. 

They have a maximal active area 60x60 mm in order to optimize the acceptance, and a narrow pitch down 

to 25 µm in order to keep the occupancy on the level of a few %. This layout ensures a high efficiency 

for track finding and double track separation. 

Guard Ring 

Structure 

22 X 

22 X 

p-comp 

+ V 

bias 

Aluminum 

Implant 

n + 

n side strip direction 

p + 

n- 

p side strip direction 

is orthogonal to the page 

Figure 2.19: Double sided ac coupled n-type strip detector with punch through biasing, guard ring structures, 

and large area compensation implantation at the n-side. 

The strips on the two sides of a sensor are tilted by an angle of 15 ◦ with respect to each other to obtain a 

stereo topology which provides two-dimensional space-point resolution. The AC coupling of the sensors 

strips is accomplished by insulating the p + - and n + -implants from the Al readout electrodes by a very 

thin SiO2 layer. Neighbored n-strips are insulated from each other by (over)compensating the electron 

accumulation layer by a large area p-implantation. Two bias rings surround the active detector region 

both on the p- and the n-side. Since strips are separated from the bias lines by the field oxide, controlled 

biasing is obtained by punch through injection of free carriers. The sensors will have a guard ring 

structure that is optimized to avoid breakdown in the termination of the depletion zone. Active area 

and guard region size may alter until the final design. The main efforts will be concentrated on the 

development of technology for production of very thin, up to 100 µm Si-strip sensors. Sensors as thin as 

150 µm are planned to be developed during R&D. Studies of their radiation resistance are in progress. 

A summary of the Si-strip STS’s major characteristics is given in Table 2.4.


Feature Value / Quantity 

Angular coverage 50 to 500 mrad 

Number of superlayers 4 

Views in one superlayer 0 ◦ , - 15 ◦ 

Detector modules per plane 28 - 60 

Double sided sensors per plane 44-184 

Detector thickness ≤ 100-150 µm 

Sensitive detector area 20 × 20cm 2 to 50 × 50cm 2 

Readout chips per module 20 

Analog (optical fibre) links 20/34 

Operational temperature ≤40 ◦ C 

Total active area 2 m 2 

Total number of readout channels 0.5 × 10 6 , 

Total number of readout chips 10 4 , 

Total number of detector modules 176 

Total number of double sided sensors 456 

2.3.4 Si-strip STS Readout Electronics 

2.3.4.1 General considerations 

Table 2.4: Summary of Si-strip STS 

The main goal of the R&D on the Si-Strip readout electronics is to achieve the new data-driven architecture, 

which is required by the CBM data acquisition and online event selection architecture (see 

section 10.1). The electronics has to operate at interaction rates of 10 MHz for A-A collisions and several 

100 MHz for p-p without a fixed bunch structure. As a consequence, hits have to be detected without 

assistance of an external trigger system or a bunch crossing clock and tagged with a time stamp. Since 

all hits will be transfered to the DAQ system, efficient techniques for sparse readout and adequate output 

bandwidth have to be provided. 

With a total number of about 10 6 readout channels, the efficient processing of detector signals has to rely 

on custom-made front-end chips which will be mounted very close to the detectors. 

The main features of the Si-Strip readout electronics are: 

• Amplitude and time measurements, 

• Digitization of amplitude and time, 

• Self triggering and generation of an event time stamp, 

• Dead time free, 

• Calibration (test) system, 

• Flexibility of control and adjustment. 

From the construction point of view the proposed layout is based on the intention to move as much electronics 

as possible from the detector site to the electronics hut without sacrificing system performance.


Digital readout using discriminators has been discarded since this choice would exclude continuous 

on-line monitoring of analog signals which will be valuable to detect radiation damage effects and to 

counteract to them. An important point is the transmission system of the signal from detector area to the 

front-end electronics. 

2.3.4.2 Si-strip Readout Chip 

The CBM detector is designed to run at a rate of up to 10 7 heavy-ion reactions per second without fixed 

bunch structure. These conditions require the development of specific front-end electronics as a part of 

the Data Driven Architecture. The most important part of the readout chain is the readout chip, which is 

placed at the Si-strip STS detector area. 

The major specifications for the chip performance are constrained by 

• Small signal - 7000 electrons per MIP for 100 µm sensor thickness; 

• Detector capacitance in the range 30-300 pF depending on the thickness and length of the strip; 

• Signal-to-noise ratio better than 10 for MIP; 

• Dynamic range of a few MIPs; 

• Random input signal rate of several 100 kHz per channel; 

• AC detector input coupling, possible DC coupling for detector dark current control; 

• Low power consumption on the level of a few mW/channel; 

• Number of channels dictated by the tracker design (128,256...) 

• Minimal number of external components; 

• Radiation hardness; 

The front-end electronics at the silicon strip detectors should provide the following functionality: 

• Finding the detector signals produced by the particles 

• Dead time free data readout from the silicon strip detectors which allows to define the detector 

signal amplitude and time of an event for each channel 

• Digitization and digital treatment of signals, including data compression and data transmission to 

the concentrator 

• Service, control, test, signal and voltage distribution functions. 

The general structure of the Si-strip STS readout chip is presented in Fig. 2.20. Each FEE channel 

consists of three parts: analog front-end, digitization plus digital back-end. 

The Strip FEE development is currently still in the first part of the conceptual design phase, where the 

design space is explored and the essential R&D issues are identified. Two different options for the 

readout chip structure are under investigation: an "analog" FEE version and a "digital" version. 

The "analog" FEE version provides functions such as signal finding, amplitude and time measurements 

mostly with analog signal processing. By further digitization one obtains the amplitude and time of the


Silicon 

Detectors 

High 

Voltage 

Distribution 

Charge 

Sensitive 

Amplifiers 

Read Out Electronics 

Analog 

Signal 

Processing 

Control 

And 

Control 

Distribution 

Digitizing 

Slow 

Control 

Housekeeping 

Digital 

Signal 

Processing 

Service and Control Electronics 

Interface 

Transmission 

System 

Test 

And 

Calibration 

System 

Amplitude 

Figure 2.20: General structure of the readout chip of the Si-strip STS. 

Input 

protection 

Ccal 

Switch 

array 

DAC 

array 

Test 

data 

Polarity 

Switch 

Calibration (test) System 

CSA 

Feedback 

adjustment 

Soft 

Limiter 

Limitation 

adjustment 

T-Pulse Fast 

shaper 

DAC 

CR-RC n 

Shaper 

(n=2) 

Peak time 

adjustment 

Analog finder 

Threshold 

Scale 

amplifier 

Gain 

adjustment 

Time 

To ADC 

Figure 2.21: Analog front-end section of one channel for the "analog" option 

hit. The analog front-end part for "analog" option version is shown in Fig. 2.21, the digitization and 

digital back-end part in Fig. 2.22 

An input protection circuitry protects the Charge Sensitive Amplifier (CSA) against possible high voltage 

discharges of the silicon detectors during bias processes. This circuitry should not increase the CSA 

noise. A polarity switch allows to handle signals of both polarities, so that the same read-out chip can be 

used for both sides of the double-sided sensors. 

The fast part of each channel provides hit finding and the start of the time measurement and consists 

of a fast shaper and a comparator. The slow part provides the amplitude measurement and contains a 

shaper and circuitry for determining and storing the amplitude. The analog memory can be organized as 

a pipeline, allowing to implement various strategies to share ADCs between channels. 

The test and calibration system provides information on the status of the FEE and the detectors, and 

produces a set of test pulses with different amplitudes for each channel. It allows to measure the crosstalk,


Internal 

pulser 

(phase control) 

ADC Pedestal 

B 

U 

F 

Subtraction 

F Pedestal 

E 

R 

memory 

External 

CLK 

Pedestal 

calculator 


measurement 

mode 

Control 

Noise 

reduction 

Shape 

reconstruction 

Signal 

finder 

Zero 

suppression 

Amplitude 

& Time 

reconstruction 

Data 

reduction 

Interface 

(serial) 

Figure 2.22: Digitization and digital back-end section for the "analog" option 

and shift of the base line and other parameters. 

The ADC and the digital part of "analog" option version provide (i) amplitude and time digitization by 

ADC for amplitude and time measurement, (ii) zero suppression and data reduction to minimize the data 

volume for transmission to the concentrator by a fast serial interface, (iii) and pedestal subtraction. 

In the "digital" FEE version the determination of amplitude and time estimates for a hit is done after 

digitization. Figures 2.23 and 2.24 show the analog front-end and digitization and digital back-end part, 

respectively, for this variant. 

Input 

protection 

Ccal 

Switch 

array 

DAC 

array 

Test 

data 

Polarity 

switch 

Calibration (test) System 

CSA 

Feedback 

adjustment 

CR-RC n 

shaper 

(n=2) 

Peak time 

adjustment 

T-Pulse Fast 

shaper 

DAC 

Analog finder 

Threshold 

Peak 

detector 

Delay 

(adjust.) 

Pipeline 

From 

pulser 

To ADC 

Figure 2.23: Analog front-end section of one channel for the "digital" option 

The analog front-end section may contain also in this scenario a fast shaper and discriminator. This allows 

for example to gate the ADC and digital signal processing stages and can thus help to reduce the power 

consumption (a similar approach is also discussed in section 9.3 ). The data driven approach requires 

that the hit detection and sparse read-out functionality are implemented on the FEE chip. However, other 

digital signal processing steps, in particular the time and amplitude estimation, can also be moved to


ADC 

Internal 

pulser 

(phase control) 

B 

U 

F 

F 

E 

R 

External 

CLK 

To pipeline 


subtraction 


memory 


calculator 


Measurement 

Mode 

Control 

Zero 

suppression 

Time 

measurement 

Data 

reduction 

Interface 

(serial) 

Figure 2.24: Digitization and digital back-end section for the "digital" option 

later stages of the FEE-DAQ chain. Currently, several options are studied to determine the impact on 

data rates, power dissipation, chip area and design complexity. 

2.3.5 Working packages, timelines, cost estimate 

The future R&D efforts will be focused on the development of (i) the technology of thin (up to 100 µm 

thickness) Si-strip sensors, and (ii) of the dedicated readout ASIC for the Si-STS based on data driven 

architecture. In addition, design studies are planned for the mechanical construction of the Si-strip STS 

based on lightweight carbon fiber monolithic space frame (ladder), which provides an excellent ratio 

of rigidity vs. mass [61]. Moreover, we plan irradiation tests of the Si-strip sensors and the FEE. The 

detailed R&D programme is listed in table 2.5. A cost estimate of the Si-Strip Tracker is given in table 

2.6.


Activity Duration 

Simulation and optimization of Si-strip STS 2005-2006 

Final Si-strip STS design 2007 

Si-strip sensors R&D 2005-2006 

Si-strip sensors prototypes 2006-2007 

Si-strip sensors radiation hardness tests 2006-2008 

Si-strip sensors baseline design 2009 

Si-strip sensors production 2009-2010 

Long Ladder technology study 2006-2007 

Long Ladder technology support, bridges design 2007-2008 

Test of prototypes of Long Ladder detectors 2008-2009 

Production of Long Ladder Si-strip detectors 2009-2010 

Front-end readout chip evaluation 2005-2006 

Front-end readout chip prototyping and test 2006-2007 

Front-end readout board design 2007-2008 

Front-end readout production and integration 2009-2010 

Mechanics, support design 2006-2007 

Cooling system design 2006-2007 

Mechanics system production and integration 2008-2010 

Si-strip STS integration, test measurement and commissioning 2011-2012 

Table 2.5: Si-strip STS Time plans. 

Sensors fabrication 3000 

Long ladder technology detectors production 500 

Front-end electronics 2000 

Mechanics, support 500 

Cooling system 500 

Infrastructure 200 

Table 2.6: Si-strip STS costs (in k).

3 Ring Imaging Cherenkov detector (RICH) 

3.1 Design considerations 

The Ring Imaging Cherenkov detector (RICH) is designed to provide electron identification in the momentum 

range of electrons from low-mass vector-meson decays (see fig 3.1). The (y, pt) region of the ρ 

and φ meson which will be covered by a certain momentum range of identified electrons is presented in 

fig. 3.2. Since midrapidity lies at 1.75, 2, and 2.16 for 15, 25, and 35 AGeV beam energy, respectively, a 

momentum range for electron identification up to 10-12 GeV/c should be sufficient. These requirements 

define possible radiators for the RICH detector: Assuming that pions can be separated from electrons up 

to 90% of the maximum Cherenkov opening angle θc (cosθc = 1 

n ), the momentum range for π identifica- 

1 

tion is illustrated in fig. 3.3 in dependence on the Lorentz factor γth = , where n is the refractive 

√ 1−1/n 2 

index of the medium. A radiator with γth > 38 would be ideal, because then the Cherenkov angle of pions 

is less than 90% of θc for all momenta smaller than 12 GeV/c. 

A second task of the RICH detector is the π identification for higher momenta in order to improve the 

K/π separation which quickly deteriorates for p > 4 GeV/c if only time-of-flight information is used (see 

fig. 14.3 and 14.5). For γth = 35.8 the momentum threshold for Cherenkov light production of pions lies 

at 5 GeV/c while kaons emit Cherenkov light only for momenta larger than 17.7 GeV/c. 

Combining both requirements for the RICH detector, a radiator with a threshold γth 38 would be 

appropriate. For a further discussion of this topic see section 3.4.2. 

entries 

10 

2 

10 

1 

0 2 4 6 8 10 12 14 16 18 20 

Plab [GeV/c] 

Figure 3.1: Total momentum distribution in the laboratory for electrons from the decay of ρ (black), ω (red) 

and φ-mesons (blue). The low-mass vector mesons were generated in Pluto with a thermal pt-distribution (T = 

130 MeV), transverse flow (β = 0.3c) and a gaussian rapidity distribution (σy = 0.41) in the center-of-mass frame 

of the collision. The total lab-momentum for the decay electrons was calculated for Elab=25 AGeV. 

59


p t [GeV] 

3 

2.5 

2 

1.5 

1 

0.5 

p e = 1 2 10 12 GeV/c 

0 

0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5 

ρ 

y 

p t [GeV] 

3 

2.5 

2 

1.5 

1 

0.5 

p e = 1 2 10 12 GeV/c 

0 

0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5 

Figure 3.2: (y, pt) region for ρ and φ mesons with lines of constant momenta for the decay-electrons. These 

lines enclose the region to be covered depending on the momentum region of identified electrons. [Center-of-mass 

rapidities for 15, 25, and 35 AGeV beam momentum are 1.75, 2, and 2.16, respectively.] 

p [GeV] 

30 

25 

20 

15 

10 

5 

K threshold 

CO 2 

N 2 

Θ π 90% of Θ c 

π threshold 

0 

0 10 20 30 40 50 60 

γ th 

Figure 3.3: Momentum threshold for 

Cherenkov light production for pions and 

kaons in dependence on γth. Also shown is the 

momentum at which the opening angle of pions 

corresponds to 90% of the opening angle 

of electrons. The green/grey band thus indicates 

the approximate region of pion identification 

in dependence on γth. γth for CO2 and 

N2 is indicated by the dashed lines. 

The overall length of the detector is mainly constrained by the maximum distance between the last STS 

and the first TRD station with which the global tracking still works sufficiently precise. On the other 

hand, a long detector will increase the number of Cherenkov photons generated by particles traversing 

the RICH since this number is proportional to its length. A 2.2 m long gas radiator is aimed at currently. 

The same polar angle as for the STS should be covered. 

In the design of the RICH detector, one has to consider that it will be operated in a high track density 

environment. The material budget has to be kept low in order to reduce secondary particle production. In 

particular, the probability for γ-conversion producing electron-positron pairs which is the largest source 

of background in the RICH detector has to be kept small. 

The overall design of the RICH detector is shown in fig. 3.4. The detector will be positioned behind the 

φ 

y

3.2. Particle identification with the RICH detector 61 

dipole magnet about 1.5 m downstream of the target. It will consist of a 2.2 m long gas radiator with a 

beam pipe in the center, two arrays of spherical hexagonal Beryllium-glass mirrors, two photodetector 

planes and corresponding support structures. The Be-glass mirrors are chosen to provide excellent optics 

of the RICH detector with a low material budget at the same time. Optics degradation and radiator gas 

pollution due to long exposition in a radiation hard environment as given in this experiment should also 

be prevented by this choice of mirrors. The photodetectors are shielded by the dipole magnet yoke to 

reduce the background from particles crossing the photodetector plane. The main requirements for the 

photodetector are high granularity, high geometrical efficiency and high detection efficiency of photons, 

in particular in the near UV region, as well as a reliable operation. The readout of the RICH detectors 

can be accomplished based on gaseous detectors with a suitable converter for UV photons or with 

photomultipliers. In this report we concentrate on a design based on the latter option. 

Figure 3.4: Overall design of the RICH detector. 

3.2 Particle identification with the RICH detector 

Particle identification with the RICH detector is performed by a measurement of the Cherenkov angle/ 

ring radius and the momentum of the particles (see fig. 3.14). Assuming that tracks with momentum are 

provided by the tracking system, the RICH part for particle identification requires the following steps: 

• ring finding 

• determination of center and radius of ring/ Cherenkov angle 

• matching of rings with tracks 

Methods currently available for ring finding and fitting are described in section 13.5 and 13.6. Here, the 

status of first simulations determining electron identification efficiency and pion misidentification will 

be described.


Purity, efficiency and momentum range of particle identification with the RICH detector is limited by: 

• refractive index of the radiator 

• Cherenkov angle/ ring radius resolution 

• momentum resolution of the tracks, position resolution of the track extrapolation to the photodetector 

plane 

• ring-track matching 

The momentum range which is available for particle identification has already been discussed in the 

previous section, see also fig. 3.3. However, the upper momentum limit for a reliable e/π separation is 

strongly determined by the Cherenkov angle/ ring radius resolution. The resolution of the reconstructed 

Cherenkov rings/ angles has the following main contributions: 

• chromatic dispersion: the wavelength dependence of the refractive index leads to a wavelength 

dependence of the Cherenkov angle, see fig. 3.31 and 3.32. The resolution of the Cherenkov angle 

will thus depend on the wavelength region detected by the photodetector. 

• pixel size: the finite granularity of the photodetector results in a corresponding finite resolution. 

The granularity is to be chosen in accordance to the other contributions to the total resolution. 

• emission point: the particle trajectories do not pass through the center of curvature of the mirror 

yielding to a smearing of the projected ring in dependence on their azimuthal and polar angle. 

• mirror surface: a deviation of the mirror surface from the ideal spherical surface yields to an 

enlargement of the focal spot in the focal plane. 

A detailed study of the contributions from these sources has to be performed. The result will be important 

for determining the optimum granularity of the photodetector, the wavelength region ideally to be 

detected, and the maximum variation of the mirror surface from the ideal spherical surface. The impact 

of the total resolution on the particle identification capabilities has still to be studied in detail. 

The matching of rings and tracks is difficult due to the high particle multiplicities in central Au+Au 

collisions at 15 to 35 AGeV; see chapter 12. The resulting challenge for RICH is illustrated in figure 

3.5: All charged tracks being reconstructed by the tracking system are reflected at the mirror in order 

to give the center of a possible Cherenkov ring (red crosses). The number of these tracks is much 

larger than the number of particles really producing a ring. In order to combine rings and tracks each 

ring is matched to the track having its extrapolation closest to the calculated ring center. The distance 

between track extrapolation and ring center ΔR is shown in fig. 3.6 for all "true" combinations, i.e. for 

those for which ring and track indeed belong to the same particle (green and black line). For ideal 

tracking (perfect momentum and position determination) the width of this distribution is mainly due to 

the limited accuracy of the ring center determination. Currently we use a simple procedure which still 

can be improved. Taking an experimental momentum resolution into account (Δp/p = 1%, azimuth 

angle β=1.3 mrad, deep angle α=0.8 mrad, see section 13.2.4) and an expected position resolution of the 

tracks at the mirror (200 µm), this distribution widens, see right part of fig. 3.6. 

However, as will be discussed in the next section, only about 1 

3 of all rings stem from primary particles, 

i.e. from those produced in the Au+Au collision. All other rings are mainly produced by electrons 

generated in secondary interactions such as γ-conversion in the material before the RICH detector. Very 

often these tracks will not be reconstructed by the tracking system. Due to the high track density another 

track extrapolated to the photodetector will probably be close by and thus matched to the ring. These

3.2. Particle identification with the RICH detector 63 

y, cm 

250 

200 

150 

100 

50 

0 

-50 

-100 

-150 

-200 

-250 

-150 -100 -50 0 50 100 150 

x, cm 

Figure 3.5: Sample event with fired 

PMTs (blue circles) and reconstructed 

track projections onto the photodetector 

plane (red crosses) in central Au+Au 

collisions at 35 AGeV. The size of 

the photodetectors is indicated by the 

boxes. These boxes contain all rings 

from primary tracks, rings lying outside 

stem from secondary interactions. 

"false" matches in dependence on ΔR are shown by the red and blue line in fig. 3.6. Naturally their 

number increases with ΔR. Every possibility for a reduction of the material budget in front of the RICH 

detector should thus be used (see section 3.3.4 for a brief discussion). 

The distance ΔR between ring center and track extrapolation will therefore be an important cut criterion 

to limit the contribution of wrong ring-track matching in the data (fig. 3.8). On the other hand, a cut on 

ΔR will limit the identification efficiency for "true" matches (fig. 3.7). 

The quality of the invariant mass spectra of the low mass vector mesons is strongly correlated to the 

amount of pions misidentified as electrons (see chapter 16). The above described investigations can be 

used for a first estimate of the π-misidentification expected from the RICH detector. Considering only 

tracks originating from the main vertex of the collision and integrating all momenta up to 12 GeV/c, the 

efficiency of electron identification is shown in fig. 3.7 in dependence on a cut in ΔR. The probability 

of matching a pion track to an electron ring is given in fig. 3.8 in dependence on a cut in ΔR. In central 

Au+Au collisions at 35 AGeV about 827 pions are produced in UrQMD simulations (see section 12). A 

preliminary estimation of the π-misidentification thus yields ∼ 4 · 10 −4 for ΔR = 0.8 cm. Combining the 

RICH electron identification with the π-suppression of the TRD stations and the particle identification 

by TOF, the final purity of the electron identification in CBM will be significantly better.


# /event (RICH) 

5 

4 

3 

2 

1 

true matching - all 

true matching - e 

false matching - all 

false matching - π as e 

0 

0 1 2 3 4 5 

Δ [cm] R 

# /event (RICH) 

3 

2.5 

2 

1.5 

1 

0.5 

true matching - all 

true matching - e 

false matching - all 

false matching - π as e 

0 

0 1 2 3 4 5 

Δ [cm] R 

Figure 3.6: Distance ΔR between ring center and track extrapolation assuming perfect tracking (left), and a final 

experimental tracking resolution (right) (see text for numbers). The black and green lines shows ΔR if ring and 

track belong to the same particle "true matching", the red and blue lines if not "false matching". Only primary 

vertex tracks (production vertex of track within 1 mm of target) with p < 12 GeV/c were considered. The latter 

requirement reflects the upper momentum border for particle identification in the RICH detector. The simulation 

was performed for central Au+Au collisions from UrQMD at 35 AGeV and 40%He+60%CH4 as a radiator. 

Efficiency 

1 

0.8 

0.6 

0.4 

0.2 

0 

0 1 2 3 4 5 

ΔRΔ[cm] 

[cm] 

Figure 3.7: Efficiency for correctly matching 

electron rings and tracks in dependence on a cut in 

ΔR; calculated from the green lines in fig. 3.6 (solid 

line for perfect tracking, dashed line taking experimental 

resolution into account). 

R 

id as e/event (RICH) 

π 

# 

5 

4 

3 

2 

1 

0 

0 1 2 3 4 5 

ΔRΔ[cm] 

[cm] 

Figure 3.8: Number of electrons per event measured 

in the RICH detector and wrongly matched to 

a pion track in dependence on a cut in ΔR; calculated 

from the blue lines in fig. 3.6 (solid line for 

perfect tracking, dashed line taking experimental 

resolution into account). 

Once more we want to emphasize the preliminary status of the simulation and these estimates. In the 

future more realistic simulations are urgently needed. Also a detailed study of the particle identification 

capabilities for π and µ is necessary. 

R

3.3. RICH simulations 65 

3.3 RICH simulations 

Simulations were started to study in detail the performance of the RICH detector. In this section, the input 

parameters of the simulation are described before the response of the RICH detector to single particles 

as well as to UrQMD events representing typical heavy-ion collisions is discussed. 

3.3.1 Description of simulation 

The performance of the RICH detector was studied within the Monte Carlo framework cbmroot [62] 

(version OCT04) using GEANT3 for the detector simulation. The implemented model of the detector 

is a simplified version of fig. 3.4 and consists of a gas vessel made of 0.25 mm aluminum walls filled 

with the Cherenkov radiator gas. Two rectangular sectors of the spherical mirror are positioned in the 

downstream part of the gas vessel. They are made of 3 mm Beryllium covered by 0.5 mm of glass. The 

curvature radius of the mirror is 450 cm. The photodetector consists of two rectangular planes positioned 

inside the gas vessel on its upstream wall. In the center of the gas vessel a hole is left for the beam-pipe. 

The photodetector was split into circle cells of 6 mm diameter corresponding to photomultiplier tubes 

assembled in a hexagonal close packing (corresponds to 90% geometrical acceptance). Only photons 

hitting the cells are considered as detected taking additionally the quantum efficiency of the photodetector 

into account. The RICH geometry used in the simulation is shown in fig. 3.9. 

Figure 3.9: RICH detector as implemented into the CBM simulation framework. 

For the simulation of Cherenkov radiation, optical properties for the radiator gas and the mirror were 

introduced to the model, as well as the quantum efficiency of the photodetector. The radiator gas refractive 

indices are taken from table 3.1, the chromatic dispersion is not yet taken into account. The 

gas transparency is based on figs. 3.29 and 3.30. The mirror reflection follows fig. 3.10 which is taken 

from the HADES experiment. No diffuse reflection is simulated yet. The photo-multiplier quantum efficiency 

is taken from fig. 3.35 which is a very optimistic scenario. Alternatively, the quantum efficiency 

from the Hamamatsu flat panel multianode PMT (H8500) [63] is used. The amplitude response of the 

photomultipliers to single photo-electrons is parametrized by fig. 3.33. 

With these parameters Cherenkov light emission is simulated for single electrons traversing the RICH 

detector. For a 2 m long nitrogen radiator the spectrum of the generated Cherenkov light is shown in 

fig. 3.11. Photon losses due to absorption in the gas, the final reflectivity of the mirror and the quantum 

efficiency of the photodetector (PMT FEU-Hive) are also presented. Using the quantum efficiency from 

the H8500 PMT from Hamamatsu, the final photon yield is reduced to about 1 

3 .

panels 

setup 

th 45 

D for 

the 

was 

each 

of all 

and 

alues 

erage 

n 0.2 

was 

re in 

t and 

tion. 

f the 

SE80 

5–0.8 

VUV 

ples 

The 

cal a- 

. The 

of a 

mple. 

probhigh 

than 

icrothe 

Fig. 662. Surface scans of polished glassy carbon (Sigradur Ring Imaging Cherenkov detector (RICH) 

s G) 

and float glass samples measured with an a-step profiler. The 

defects shown in the top curve cover less than 0.1% of area. 

Fig. 3. Reflectivities measured for witness substrates of polished 

glassy carbon (Sigradurs G) and float glass covered with 

Al=MgF2: 6 

] 

-1 

The VUV reflectivity was measured in a monochromator 

setup with a D2 lamp and a solarblind 

photo tube as described in Ref. [8]. The experimental 

curves are shown for the wavelength region 

of interest in Fig. 3 and exhibit constant values 

around R ¼ 80% down to 150 nm: The reflectivity 

curves show no significant difference between float 

glass and polished Sigradurs dN 

2 

= 

2πα 

sin θ L 

dλ 

2 

λ 

generated photons 

5 

reflected photons 

measured photons 

4 transmitted photons 

G and are consistent 

with 3the 

assumption of a micro roughness sC 

2 3 nm as deduced from the ratio of observed 

specular reflectance to that of an ideal smooth 

2 

surface [9]. 

[nm 

λ 

dN/d 

1 

0 

100 200 300 400 500 600 700 

wavelength λ [nm] 

Figure 3.10: Reflectivity of the 

HADES mirror in dependence on the 

wavelength [64]. This reflectivity was 

used for the simulation. 

Figure 3.11: Spectrum of the 

Cherenkov light emitted by single 

electrons traversing a N2 radiator. As 

black curve the spectrum of generated 

photons is shown, the red line shows 

those which are transmitted by the 

nitrogen. In green the reflected photons 

are illustrated and in blue those 

being detected by the photomultipliers 

taking a quantum efficiency as shown 

in fig. 3.35. An integration of this 

spectrum yields the number of photons 

finally detected in the PMTs. 

Fig. 3.12 shows the focussed Cherenkov rings from electrons in one quarter of the photodetector plane. 

For this simulation no magnetic field was applied and no multiple scattering was allowed. Hits from all 

photons are shown not taking into account the granularity and quantum efficiency of the photomultiplier. 

In the center of the photodetector the rings are well focussed while they are not in the outer regions. 

In future, the tilting of the mirrors and photodetector planes has to be changed to optimize the imaging 

properties of the mirror. Also, the acceptance has to be better matched to the one of the STS. For a brief 

discussion of the Cherenkov angle/ ring radius resolution and its implication on the detector design see 

section 3.2. 

3.3.2 Response of the RICH detector to single particles 

To study the RICH response to single particles crossing the detector, single electrons, muons and pions 

were simulated. They were emitted from the center of the target with a uniformly distributed trans-


one quarter of mirror/ photodetector: 

φ = 80 o 60 o 40 o 

20 o 

θ = 5 o 10 o 15 o 20 o 25 o 30 o 35 o 

Figure 3.12: Imaging 

property of the RICH detector 

as it is currently implemented 

into the simulation. 

Tracks are emitted from the 

target, θ is their polar, φ their 

azimuth angle. 

verse momentum 0 < pT < 5 GeV/c, and a uniformly distributed azimuth and polar angle, the latter 

being in the range of 2◦ < θ < 30◦ . The number of fired photo-multipliers versus the particle momentum 

for electrons, muons and pions were simulated for three radiator gases, 40%He+60%CH4, N2 and 

50%N2+50%CH4 (fig. 3.13). The average number of photomultipliers per electron giving a signal is 26, 

30 and 33, respectively, assuming the quantum efficiency for the PMT-FEU Hive as shown in fig. 3.35. 

For the Hamamatsu H8500 PMT these numbers are reduced to about 1 

3 . Since the Cherenkov threshold 

for muons and pions is at p = 4 − 6 GeV/c depending on the gas, their Cherenkov angles are momentum 

dependent resulting in a momentum dependent number of PMTs giving a signal. 

NPMT 

50 

e± 

45 

40 

π ± 

± 

μ 

35 

30 

25 

20 

15 

10 

5 

2 4 6 8 10 12 14 

p, GeV/c 

NPMT 

50 

e± 

45 

40 

π ± 

± 

μ 

35 

30 

25 

20 

15 

10 

5 

2 4 6 8 10 12 14 

p, GeV/c 

NPMT 

50 

e± 

45 

40 

π ± 

± 

μ 

35 

30 

25 

20 

15 

10 

5 

2 4 6 8 10 12 14 

p, GeV/c 

Figure 3.13: Number of PMTs giving a signal versus the track momentum assuming a quantum efficiency 

of the PMTs as shown in fig. 3.35. e ± are shown in red, µ ± in blue, and π ± in green for three radiator gases 

40%He+60%CH4, N2, and 50%N2+50%CH4. 

The photomultipliers fired by the focussed Cherenkov light of one particle were fitted by a circle equation. 

The radius distribution for electrons, muons and pions is shown in Fig. 3.14 in dependence on the 

particle momentum. The electron ring radius is about 5.5 cm independent on the particle momentum and 

slightly depending on the chosen radiator gas. Starting from about 10-12 GeV/c the radius distribution of 

muons and pions overlaps with the distribution of electrons limiting the electron identification capability 

of the RICH detector. A detailed investigation of the particle identification capabilities in dependence on 

the momentum has still to be performed, preliminary results have been discussed in section 3.2.


R, cm 

7 e± 

π ± 

± 

6 

μ 

5 

4 

3 

2 

1 

2 4 6 8 10 12 14 

p, GeV/c 

R, cm 

7 e± 

π ± 

± 

6 

μ 

5 

4 

3 

2 

1 

2 4 6 8 10 12 14 

p, GeV/c 

R, cm 

7 e± 

π ± 

± 

6 

μ 

5 

4 

3 

2 

1 

2 4 6 8 10 12 14 

p, GeV/c 

Figure 3.14: Ring radius distribution versus the track momentum for e ± (red), µ ± (blue) and π ± (green) for the 

three radiator gases 40%He+60%CH4, N2, and 50%N2+50%CH4. 

P 

0.8 

0.7 

0.6 

0.5 

0.4 

0.3 

0.2 

0.1 

0 

0.5 1 1.5 2 2.5 3 3.5 4 4.5 5 5.5 

Np.e. 

Figure 3.15: Number of detected photons per 

photodetector including the quantum efficiency of 

the PMT-FEU Hive; only those photodetectors 

are considered which were hit by at least one 

Cherenkov photon from an electron crossing the 

RICH detector. 

In fig. 3.15 the number of detected photons per photodetector including the quantum efficiency of the 

PMT-FEU Hive is shown. For this figure only those PMTs were considered which were hit by at least 

one Cherenkov photon from single electrons crossing the RICH detector. Approximately 15% of all 

PMTs detect two photons. 

3.3.3 Acceptance of the RICH detector 

For acceptance studies of the RICH detector single particles were generated with a uniformly distributed 

transverse momentum, azimuthal angle and rapidity. The particle is accepted in the RICH detector if at 

least 3 photo-multipliers detected Cherenkov photons from this particle. Since for particle identification 

the momentum is also needed, at least 4 hits are required in the tracking system STS. In the final layout 

the RICH acceptance will be matched to the STS acceptance. 

The acceptance of electrons and π − in dependence on transverse momentum pT and rapidity y is shown 

in figs. 3.16 and 3.17, respectively. The acceptance of the light vector mesons ρ,ω, and φ decaying 

into e + e − pairs is shown in figs. 3.18, 3.19, and 3.20. For each of the decay products the single particle 

acceptance criteria as described above are used. For all particles a wide range in y and pT will be covered.


[GeV/c] 

T 

p 

4 

3.5 

3 

2.5 

2 

1.5 

1 

0.5 

0 

0 1 2 3 4 5 6 

y y 

Figure 3.16: RICH+STS acceptance for e − in dependence 

on pT and y. 

[GeV/c] 

T 

p 

4 

3.5 

3 

2.5 

2 

1.5 

1 

0.5 

0 

0 1 2 3 4 5 6 

y y 

Figure 3.18: RICH+STS acceptance 

for ρ → e + e − in dependence 

on pT and y. 

1 

0.9 

0.8 

0.7 

0.6 

0.5 

0.4 

0.3 

0.2 

0.1 

0 

[GeV/c] 

T 

p 

4 

3.5 

3 

2.5 

2 

1.5 

1 

0.5 

1 

0.9 

0.8 

0.7 

0.6 

0.5 

0.4 

0.3 

0.2 

0.1 

0 

[GeV/c] 

T 

p 

4 

3.5 

3 

2.5 

2 

1.5 

1 

0.5 

0 

0 1 2 3 4 5 6 

y y 


for ω → e + e − in dependence 

on pT and y. 

3.3.4 Response of the RICH detector to heavy-ion collisions 

0 

0 1 2 3 4 5 6 

y y 

Figure 3.17: RICH+STS acceptance for π ± in dependence 

on pT and y. 

1 

0.9 

0.8 

0.7 

0.6 

0.5 

0.4 

0.3 

0.2 

0.1 

0 

[GeV/c] 

T 

p 

4 

3.5 

3 

2.5 

2 

1.5 

1 

0.5 

0 

0 1 2 3 4 5 6 

y y 

1 

0.9 

0.8 

0.7 

0.6 

0.5 

0.4 

0.3 

0.2 

0.1 

0 

1 

0.9 

0.8 

0.7 

0.6 

0.5 

0.4 

0.3 

0.2 

0.1 


for φ → e + e − in dependence 

on pT and y. 

The performance of the RICH detector has been studied for central Au+Au collisions at 35 AGeV. Events 

were generated by the UrQMD event generator and then tracked through the modelled CBM setup by 

the CBM simulation package cbmroot with GEANT3. Fig. 3.5 shows the signals of the PMTs in the 

photodetector plane for a sample event. 

The left plot of fig. 3.21 shows the distribution of the number of detected tracks in the photodetector 

plane for three radiator gases. On average about 80, 90, and 95 tracks are measured in the RICH detector 

for the radiator gases 40%He+60%CH4, N2, and 50%N2+50%CH4, respectively. Only about 1 

3 of these 

particles are primary particles generated by UrQMD in the nuclear collision (right plot of fig. 3.21). It 

should be noted that these numbers depend strongly on the size of the inner hole of the mirrors which is 

left for the fast particles. The size of this hole has still to be optimized with respect to acceptance and 

ring multiplicity. In particular, since in the inner region particles with high momenta are accepted and 

the particle identification capability of the RICH detector deteriorates above 12 GeV/c. 

A large fraction of the particles detected in RICH thus stem from secondary sources. In order to allow 

for a good identification of the light vector mesons the overwhelming combinatorial background has to 

be reduced (see section 16.1). One major background source are electrons from γ-conversion. In order to 

0


a.u. 

0.22 2 

0.2 

0.18 

N 

50%N +50%CH 

2 

4 

40%He+60%CH4 

0.16 

0.14 

0.12 

0.1 

0.08 

0.06 

0.04 

0.02 

0 

0 20 40 60 80 100 120 140 160 180 

N 

rings 

a.u. 

0 

0 5 10 15 20 25 30 35 40 45 50 

Figure 3.21: Total multiplicity of particles detected in RICH for central Au+Au collisions at 35 AGeV (left). 

Multiplicity of primary particles (generated by UrQMD) measured in the RICH detector for N2 as a radiator 

(right). 

reduce this background source, the position/ material in which the conversion took place has to be known. 

The z-position of the production vertex of all tracks measured in the RICH detector is shown in fig.3.22. 

Peaks on this distribution are due to tracks produced in the target (z = 0 cm, non-segmented), at the 7 

STS stations (z = 5,10,20,40,60,80, and 100 cm), and at the RICH front wall (z = 170 cm). The wide 

maximum between z = 20 cm and z = 80 cm stems mainly from e + e − pairs generated by γ-conversion 

in the beam pipe: Since in the current setup the first three STS stations are located in the vacuum inside 

the beam pipe, the size of the beam pipe is reduced after the 3rd STS station. Therefore all particles 

cross the beam pipe. In addition, the opening angle of the reduced beam pipe directly points back to the 

vertex, therefore γ rays with a certain polar angle are all converted in the beam pipe. The following wide 

maximum between z = 110 cm and z = 140 cm stems from e + e − pairs generated by γ-conversion in the 

magnet yoke. In future simulations the position of the magnet yoke relative to the RICH detector will 

be changed which should yield less electrons from γ-conversion. The continuous underlying distribution 

comes from secondary particles produced in the gas. The different radiation length of the studied three 

radiator gases results in a different production rate of secondaries. However, it becomes clear that for all 

gases the contribution of background tracks can be neglected compared to the sources discussed before. 

This above discussion also made clear that the material budget in front of the RICH detector has still to 

be optimized which will result in less secondaries in the RICH detector. 

The occupancy of the RICH photodetector can be represented by the number of fired photomultipliers. 

Fig. 3.23 shows the number of photomultipliers giving a signal in one central Au+Au collision at 

35 AGeV. Three RICH radiator gases, 40%He+60%CH4, N2, and 50%N2+50%CH4 are shown on this 

plot by different colors. The momentum distribution of tracks measured in the RICH detector is shown in 

fig. 3.24. For the current geometry 58 electrons, 25 pions, 0.4 muons and 6·10 −3 kaons are measured on 

average per central Au+Au event at 35 AGeV. We would like to emphasize once more that these numbers 

will change with an optimized detector geometry and material budget in front of the RICH detector. 

700 

600 

500 

400 

300 

200 

100 

N 

prim


per event 

vertex 

N 

a.u. 

0.14 

0.12 

0.1 

0.08 

0.06 

0.04 

0.02 

-1 

10 

-2 

10 

-3 

10 

-4 

10 

N2 

50%N +50%CH 

2 

4 

40%He+60%CH 

0 50 100 150 200 250 300 350 400 

z [cm] 

N2 

50%N +50%CH 

2 

4 

40%He+60%CH 

0 

0 500 1000 1500 2000 2500 3000 

4 

NPMT 

Figure 3.23: Number of photomultipliers registering 

a photon for central Au+Au collisions at 

35 AGeV. Also here the quantum efficiency of the 

PMT-FEU Hive is used. 

4 

N per event 

10 

1 

-1 

10 

-2 

10 

-3 

10 

-4 

10 

Figure 3.22: z-coordinates of 

the track vertices for all tracks 

measured in the RICH detector. 

± e : 58.066 per event 

π± 

: 25.387 per event 

± μ : 0.412 per event 

± K : 0.0057 per event 

0 5 10 15 20 25 30 

p , GeV/c 

lab 

Figure 3.24: (Momentum distribution of all tracks 

measured in the RICH detector using N2 as radiator: 

e ± (red), µ ± (blue), π ± (green), and K ± (pink) 

are shown.


3.4 Technical realization of the RICH detector 

3.4.1 Optics and mirrors 

The actual layout of the RICH detector in the vertical and horizontal plane is shown in fig. 3.25. The 

RICH detector consists of two identical spherical mirror walls. The radius of the mirror curvature is 

450 cm. Each spherical mirror has the overall dimensions 4.5x1.75 m 2 . The Cherenkov light produced 

by charged particles is focused on two photodetector planes, positioned at 25 cm just after the dipole 

magnet and in about 2.2 m distance from the mirrors. In the center of the mirror system is a whole for 

the beam pipe. 

The RICH mirrors will be made mainly of spherical hexagonal 3 mm thick Beryllium plates covered 

with 0.5 mm glass which forms an optically perfect mirror surface. The diameter (maximum size) of the 

hexagons is 60 cm. The diameter of a light point source in the focal plane containing more than 95% 

Figure 3.25: Layout of the RICH detector, left vertical plane, right horizontal plane. 

Figure 3.26: Be mirror prototype without glass and Al cover.

3.4. Technical realization of the RICH detector 73 

of the light is less than 0.45 mm. The total radiation length of the mirror (Be+glass) is 1.25% of X0. 

The weight of one hexagonal plate is about 1.3 kg. The technology of such a mirror production exists 

in Russia and has been chosen for RICH1 of the LHCb experiment; see fig. 3.26 for a prototype of the 

Be plate. The measured optical surface roughness after glass polishing, Al covering and SiO2 coating is 

σh = 1.6 nm. This roughness determines the diffuse reflection which is the total reflectivity R0 minus the 

specular reflectivity Rsp. The ratio Rsp/R0 can be calculated from σh in dependence on the wavelength: 

Rsp/R0 = exp(−4πσh/λ). (3.1) 

The specular reflectivity is shown in fig. 3.27 as function of the wavelength for a surface roughness of 

σh = 1.6 nm. The diffuse reflectivity increases in the UV region but is still only about 12% of R0 at 

λ = 150 nm. The total reflectivity R0 of Al mirrors is 92% within a wide wavelength range for photons. 

However, the coating to seal the Al has still to be chosen and matched to the wavelength dependence of 

the sensitivity of the photodetector. On average less than 92% reflectivity will thus be achieved for the 

final mirror. 

Specular reflectivity, % 

100 

80 

60 

40 

20 

0 

Be-glass mirror, specular reflectivity 

100 200 300 400 500 600 700 

Wave length, nm 

Figure 3.27: Fraction of specular reflectivity 

for the Be-glass mirrors calculated according 

to equation 3.1. 

The structure of one mirror wall consisting out of the Be-glass hexagons is shown in Fig. 3.28. Each 

hexagon will be supported separately by the common support structure of the RICH detector. Each 

segment will be moveable in order to allow for individual tuning to provide excellent optics of the mirror 

assembly as a whole. A laser monitoring during operation of the detector will allow for continuous 

quality control and readjustment if necessary. 

Figure 3.28: Illustration of the assembly of the Be-glass hexagons for one of the mirror walls.


3.4.2 Radiator 

In section 3.1, the Lorentz factor γth as the main requirement on the radiator was discussed. Possible 

gas radiators with γth 38 are listed in table 3.1; mixtures of these gases are possible as well. Besides 

γth, the radiation length is an important factor to be considered because it determines the production of 

secondary electrons which are an important background source in the RICH detector. It is also a matter 

of concern for the multiple scattering which strongly affects the track matching between STS and TRD. 

Since the number of Cherenkov photons produced by charged particles considerably increases in the UV 

region (see fig. 3.11), the UV region is important for the final photon yield per particle. However, the 

transmittance of photons in the UV range is limited by absorption in the gas, the threshold wavelength is 

given in the second last column in table 3.1. Fig. 3.29 and 3.30 show the wavelength dependence of the 

transmittance for two of the radiators as example. If no window between the photodetector and the gas 

radiator will be used, possible radiator gases have also to be checked whether they are compatible with 

this requirement. 

An important characteristic of the radiators eventually limiting the resolution of the rings in the photodetector 

plane is the dispersion of the refractive index n(λ). The chromatic dispersion is rather weak 

in a wide wavelength range, however, it changes fast in the UV region. In the Landolt-Boernstein Series 

[65] parameterizations and data can be found for the radiator gases of interest (see fig. 3.31). For a 

Radiator gas n-1 γth θc λthresh Radiation 

[10 −4 ] [nm] length [m] 

He 0.35 119.5 0.48 ◦ 50 5300 

N2 2.98 41.0 1.4 ◦


(n-1)10 4 

6 

5.5 

5 

4.5 

4 

3.5 

3 

2.5 

CO 2 

CH 4 

N 2 

2 

100 200 300 400 500 600 700 800 

λ[nm] 

Figure 3.31: Chromatic dispersion for various 

gases; data and parametrization (dotted lines) are 

taken from [65]. The solid line corresponds to an 

own fit according to formula 3.2. The dashed horizontal 

lines indicate the fixed refraction index used 

for simulation. 

temperature of 0 ◦ C and a pressure of 1 atmosphere, n(λ) can be calculated as 

Θ e [degrees] 

3 

2.8 

2.6 

2.4 

2.2 

2 

1.8 

1.6 

1.4 

1.2 

(n(λ) − 1) · 10 6 = C 

ν2 · 106 

0 − ν2 

= C′ 

1 

λ 2 0 

CO 2 

CH 4 

N 2 

1 

100 200 300 400 500 600 700 800 

λ[nm] 

Figure 3.32: Wavelength dependence of the 

Cherenkov opening angle due to the chromatic dispersion 

in the various gases. The opening angle is 

calculated from the fit with the solid line in fig. 3.31. 

with the parameters C and ν 2 0 or C′ and λ0 as given in table 3.2. With n(λ) the Cherenkov angle which is 

proportional to the ring radius can be extracted, see fig. 3.32. Taking nitrogen as example, the Cherenkov 

opening angle changes by ∼0.1 ◦ between λ = 600 nm and λ = 200 nm. The wavelength region which 

to detect in the photodetector has thus to be chosen keeping in mind the desired final Cherenkov angle 

resolution (see discussion in section 3.3). The lower limit of the UV region can be regulated by either 

choosing a photodetector working in the required wavelength region or by adding gases to the radiator 

with adsorption below the chosen limit. 

− 1 

λ 2 

Radiator gas C ν 2 0 C ′ λ0 

[10 27 cm −2 ] [10 27 cm −2 ] [nm −2 ] [nm] 

He 1.21238 34991.7 

N2 5.0345 17095 0.05432 73.63 

CH4 5.02763 11689.3 0.05412 89.13 

CO2 6.2144 14097 0.073191 77.73 

Table 3.2: Parameters for calculating the chromatic dispersion at 0 ◦ C and 1 atmosphere according to formula 

3.2. C and ν 2 0 are taken from [65], C′ and λ0 are from an own fit, see fig.3.31. 

Pure N2 as a radiator fulfills all requirements for the RICH detector. In addition it is non inflammable and 

a chemically passive gas. It satisfies security requirements as well, and the RICH gas system would be 

rather cheap in this case. Thus, we have chosen a N2 radiator as the base option for the RICH detector. 

, 

(3.2)


The radiation length of a 2.2 m long N2 radiator is equal to 0.75% of X0. A disadvantage of the N2 

radiator is that N2 is a weak fluorescent gas in the UV region [72]. However, fluorescent light is diffuse 

and the Omega RICH detector was successfully operated with a 5 m long nitrogen radiator for the WA69, 

WA82, and WA89 experiments at CERN [73]. Still, the performance of a N2 radiator in a high rate and 

high track density experiment like at CBM should be carefully studied. It was shown that an admixture 

of methane but also CO2 can suppress the fluorescent light from N2 [74]. An admixture of 40% methane 

(or CO2) could thus also be an option for a RICH gas radiator. In addition, it would limit the transparency 

to wavelengths above 145 nm (or 175 nm) thus eliminating much of the region in which the refractive 

index of N2 changes fast, see fig. 3.31. The radiation length of a 2.2 m long radiator would be 0.57% of 

X0 for 60% N2+40% CH4. However, due to the fact that methane is highly inflammable, the gas system 

would be more expensive and the safety regulations would be much more demanding than in the case of a 

pure N2 radiator. In addition, the production of slow protons due to interactions of thermal neutrons with 

Hydrogen might be a problem when using methane and needs to be studied in detail, see [260, 261, 77]. 

Studies for a N2/CO2 mixture have still to be performed. 

CO2 has a slightly lower γth and a 2.2 m long radiator would correspond to 1.2% of X0, but it might 

still be an option if the pion identification capabilities need to be increased for momenta starting from 

4.65 GeV/c instead of 5.6 GeV/c which is the threshold for N2. The most promising radiator gas in terms 

of radiation length (and the most expensive for service) is the 40% He+60% CH4 gas mixture. A 2.2 

m long radiator would be equal to only 0.22% of X0 in this case. In addition to the safety requirements 

discussed above, the He admixture would lead to hard hermetic requirements of the RICH gas system 

due to the high He leakage, resulting in an increased price of the gas system. He leakage would also be 

a matter of concern for vacuum devices used for the photodetector. 

Three gas mixtures, pure N2, 60% N2+40% CH4 and 40% He+60% CH4, were studied when simulating 

the RICH performance. Further studies, also with a RICH prototype, have still to be performed. 

3.4.3 UV photodetector 

The RICH detector has two photodetector planes each covering an area of about 2.8x1.4 m 2 . The diameter 

of the focussed electron rings is approximately 11 cm (see fig. 3.14). Requiring at least 10 pads 

per diameter, the maximum padsize should be 1 cm×1 cm. Assuming a possible range of padsizes from 

0.5 cm×0.5 cm to 1 cm×1 cm, about 3 · 10 5 to 10 5 channels would be needed. As has been shortly discussed 

in the previous chapter, the sensitivity of the photodetectors should cover as much of the visible 

wavelength as possible down to λ ≈ 200 nm. Moreover, due to the high interaction rates the detectors 

should be fast. 

3.4.3.1 Small diameter photomultipliers 

These requirements are fulfilled by using photomultipliers as basic elements of the photodetector. The 

IHEP Protvino has designed the so called PMT FEU-Hive in cooperation with the Moscow Electrolamp 

Company (MELZ). This is a special small diameter photomultiplier tube on the basis of a resistive 

distributed dynode system with electrostatic focusing, bialkali photocathode and plain glass window. The 

external diameter of the PMT FEU-Hive is equal to 6 mm, the tube length is 60 mm, more parameters 

are given in table 3.3. The electrostatic optics, construction details and PMT parameters have been 

optimized by using a computer model of the PMT. In particular by means of the optics and dynode 

system optimization one achieves an effective operation of the PMT in the one-photo-electron regime. 

This is an important feature for its possible application in RICH detectors. In fig. 3.33 the simulated 

one-photo-electron spectrum is shown for this PMT FEU-Hive. The PMT pulse jitter is on the order of 

1 ns.


Parameter Description Unit 

External sizes D 6x60 mm 

Window material C-95 glass 

Window thickness 0.5 mm 

Photocathode Material Bialkali (K2CsSb) 

Spectral Response 300 to 650 nm 

Peak Wavelength 400-420 nm 

Max. Quantum Efficiency (at 400-420 nm) 25 % 

Dynode structure Resistive distributive 

Dynode number 13 (on average) 

Capacitance 10-15 pF 

Gain 10 6 

Charge 0.16 pC 

Power dissipation 40 mW 

Anode Dark Current per Channel 0.5 nA 

Rise Time 1 ns 

Transit Time 10 ns 

Transit Time Spread (FWHM) 1 ns 

Supply (Anode to Cathode) Voltage -2000 V 

Average Anode Output Current 2 µA 

Weight 5 g 

Table 3.3: Main parameters of the PMT FEU-Hive. 

The photocathode sensitivity of the PMT FEU-Hive is typical for bialkali photocathodes, see fig. 3.34. 

However, a higher sensitivity in the UV range would be desirable. To increase the sensitivity of the photodetector 

in the UV region, one may use UV transparent photocathode windows made of quartz, MgF2 

or UV glasses. This is a standard, but unfortunately rather expensive solution. An alternative solution 

would be to cover the glass of the photocathode window with a transparent WLS film from evaporated 

layers of tetraphenil butadiene or a transparent p-terphenyl film using the technology developed and used 

at IHEP [78, 79]. According to simulations, the quantum efficiency of such an optimized photodetector 

could become flat at the 22% level in the UV range starting from 100 nm, see Fig. 3.35. The plateau can 

be understood from the flat character of the tetraphenyl butadiene or p-terphenyl WLS spectral response 

in the range from 100-350 nm [80]. Since dN/dλ ∝ λ −2 holds for the Cherenkov spectrum, the use of 

WLS films should result in an increase of detected Cherenkov photons up to a factor of 3.5, even if a 

less transparent gas is used or evaporated tetraphenil butadiene WLS of a diffuse type [81]. The use of 

WLS films thus should considerably improve the performance of the RICH detector compared with the 

standard solution of a usual bialkali photocathode. Depending on the required wavelength to be detected, 

the WLS film can also be chosen to extend the sensitivity to a smaller UV region. 

The PMTs would be assembled in two photodetector planes using special honeycomb structures made 

of carbon fibers. A schematic fragment of this structure is shown in fig. 3.36. The wall thickness 

of the honeycomb structure is equal to 0.5 mm. Together with the thickness of the PMT wall itself 

(0.5 mm of glass) this leads to an overall geometrical coverage of the photocathodes of less than 54% 

only. Although the geometrical efficiency is so low, a fraction of almost 90% of the total area of the 

photodetector plane can be made active in the RICH detector using special cone-shaped reflectors from 

aluminized plastics which collect the Cherenkov light to the active part of the photocathode window of 

each PMT. For estimating the overall detection efficiency of Cherenkov photons, the quantum efficiency 

shown in fig. 3.35 has to be scaled with the geometrical efficiency yielding about 18% of all photons for


Nevent 

200 

175 

150 

125 

100 

75 

50 

25 

0 

λ < 350 nm. 

0 2 4 6 8 10 

Amplitude (Arbitr.units) 

3.4.3.2 Alternative photodetectors 

Figure 3.33: One-photo-electron spectrum 

as simulated for the PMT FEU-Hive. 

As an alternative, multianode devices such as multianode PMTs or HPDs are very promising, because 

they would reduce the number of electronic devices considerably. Hamamatsu, e.g., offers a new flat 

panel multianaode PMT (H8500) with a 8×8 matrix, a high geometrical coverage of 89%, a pixel size 

of 5.8mm×5.8mm, and a bialkali photocathode [63]. Main parameters of this flat panel MAPMT are 

given in table 3.4. Unfortunately an increase of the sensitivity towards the UV region cannot easily be 

done with WLS films. Since the light radiation of the WLS film has an isotropic angular distribution, a 

significant part of the shifted light will propagate along the photocathode window resulting in a decreased 

position resolution for the hit position of the Cherenkov photon. In addition, the detection efficiency will 

be reduced due to internal light reflection in the glass window. Intensive R&D work on this topic would 

be needed. 

The LHCb experiment at CERN decided to use pixel-HPDs as photodetector with a pixel size of 

2.5 mm×2.5 mm on the HPD photocathode. The HPD has a 7 mm thick, spherical quartz entrance 

Radiant sensitivity, (mA/W) 

90 

80 

70 

60 

50 

40 

30 

20 

10 

0 

200 250 300 350 400 450 500 550 600 650 700 


Figure 3.34: Sensitivity of the bialkaliphotocathode 

of the PMT FEU-Hive as function of 

the photon wavelength. 

Quantum efficiency, % 

25 

20 

15 

10 

5 

0 

100 200 300 400 500 600 700 


Figure 3.35: Simulation of the FEU-Hive quantum 

efficiency with an optimized WLS window.


Figure 3.36: Fragment of the honeycomb structure in which the PMTs could be assembled. 

Parameter Description Unit 

Dimensional Outline (WxHxD) 52x52x28 mm 3 

Effective Area 49x49 mm 2 

Pixel Size/Pitch at Center 5.8x5.8/6.08 mm 

Number of Anode Pixels 64 (8x8 matrix) 

Window material Borosilicate glass 

Window thickness 2.0 mm 

Photocathode Material Bialkali 

Spectral Response 300 to 650 nm 

Peak Wavelength 420 nm 

Quantum Efficiency at 420 nm 19 % 

Dynode structure Metal channel dynode 

Dynode number 12 

Gain 10 6 

Anode Dark Current per Channel 0.5 nA 

Rise Time 0.8 ns 

Transit Time 6 ns 

Transit Time Spread (FWHM) 0.4 ns 

Supply (Anode to Cathode) Voltage -1100 V 

Average Anode Output Current in Total 100 µA 

Weight 145 g 

Operating Ambient Temperature 0 to +50 ◦ C 

Uniformity 1:3 

Cross-talk 3 % 

Table 3.4: Main parameters of the flat panel multianaode PMT H8500 [63].


window with an S20 (multialkali) photocathode deposited on its inner surface. The effective area of a 

hexagonal close packing of these HPDs is 67%; for more information also on the quantum efficiency 

see [82]. 

Gaseous UV detector 

An alternative solution to detect the Cherenkov photons is the use of a thin gaseous detector with a CsI 

photocathode. This approach is convenient for its low cost and large geometrical coverage. However, in 

order to limit the chromatic dispersion (see fig. 3.32), the use of a window in front of the detector would 

be desirable. Quartz [83] (cut-off at approximately 170 nm) or the more expensive CaF2 [84] (cut-off at 

150 nm approximately) are candidate materials for this window. In this scenario, one can decouple the 

radiator gas from the counting gas. CsI photocathodes are nowadays [83,85] produced with a QE of 30% 

at 160 nm (fig. 3.30) with an almost linear decrease to 0% at 208 nm. This range is adequate to achieve 

good spatial resolution in the ring reconstruction. The gaseous detector itself should be a few mm thin 

in order to cope with the event rate foreseen in the experiment, and to limit the energy loss of charged 

particles that might cross the detector. An anode to cathode distance of 2-3 mm is enough. The counting 

gas must be transparent to VUV photons and limit the scattering of the photoelectrons. The ALICE 

HMPID uses CH4, although mixtures of this hydrocarbon with He are conceivable. The operating gain 

should be ≦ 10 5 in order to achieve > 90% photoelectron detection efficiency. Precisions of order 100 

µm are thus needed in the positioning of the electrodes for a stable operation of the chamber. Assuming a 

grid of 70 µm diameter cathode wires at 4 mm pitch positioned just behind the window, the geometrical 

acceptance would be larger than 98%. The readout electronics must combine short shaping time with 

good signal to noise. In this respect one can benefit from the developments foreseen for other detectors 

of the experiment, like the TRD (see chapter 4). 

These different photodetector types which could be used for the RICH detector have to be studied in 

detail. As for all the other elements of the RICH detector, a stable operation is a major issue. The 

detector simulations which have been started in order to give precise answers on the granularity needed 

for a sufficient Cherenkov angle resolution and the wavelength region which should ideally be covered 

by the measurement. 

3.4.4 HV and readout electronics 

For the FEU-Hive photomultiplier a HV control device and readout electronics have been developed. 

The HV control device is designed on the basis of the classical scheme, i.e. a ballast resistor Rb in 

connection with the PMT high voltage divider, see fig. 3.37. The resistor Rb, being made of a set of 

consecutive resistors Rn with values 2 n R, can be tuned to change the high voltage applied to the PMT 

by means of switching transistors, controlled by optopairs according to the code in a status register, see 

fig. 3.38. The voltage drop on the control resistor Rb should be about 400 V (maximum 500 V). Using 

a 6-bit commutator for changing the value of the control resistor, the PMT high voltage can be tuned in 

steps of 7 V. The requirements on the FEE of the RICH detector are determined by the main features 

of the output signals from the PMTs, i.e. the dynamical range of the total charge, noise current, signal 

time and output chain capacitance. For the PMT FEU-Hive the dynamical range of the signal charge is 

Q = (0.25 − 25) · 10 6 e − = 0.04–4 pC, the average signal time is about 1 ns; other parameter values can 

be found in table 3.3. An additional amplification of the output signal of the PMT is needed. The shaping 

time of the amplifier is determined by the chosen sampling frequency fsampl in the ADC. If the minimal 

signal sampling is set to 3, the amplifier shaping time should be on the order of τshaper = 3/ fsampl = 50 ns 

at fsampl = 60 MHz. 

Concerning the ADC bit number the RICH requirements are sufficiently weak. Due to the strong over-


HV 

R 

b 

R 

PM 

Control 

Figure 3.37: High voltage control scheme. 

HV 

R 

2R 4R 8R 16R 32R 

R PM 

A B C D E F 

Status Register 

BUS 

Control 

Figure 3.38: Control resistor and commutator 

scheme of the PMT HV regulation. 

lapping one-photo-electron and two-photo-electron signal spectra in the PMT FEU-Hive, the digitization 

of the output signals is important only for an individual tuning of the threshold in all RICH channels. A 

signal digitization with an 8-bit ADC is therefore sufficient. 

The HV supply and readout electronics of the other possible photodetectors discussed in the last section 

would still have to be developed. Since other new big-scale experiments like LHCb and BTeV will start 

before the final design of the CBM RICH detector has to be available, a lot of knowledge gained in those 

experiments could be used for CBM. 

3.4.5 Support structure, gas vessel and gas supply system 

In general, the support structure of the RICH detector consists of two main parts: a support system for 

the two photodetector planes and a support system for the two Be-hexagon walls. For both a rectangular 

frame would be used in order to position all structural components outside of the spectrometer 

acceptance. Each mirror hexagon will be supported separately from the back and can be rotated in two 

dimensions for tuning the optical system. 

In the center of the RICH detector a vacuum beam pipe will be installed in order to prevent the interaction 

of beam particles with the radiator gas. The overall support structure of the RICH detector including the 

front part of the photodetector planes as well as the mirror walls with mechanics are placed in one gas 

vessel. The approximate vessel size can be extracted from figure 3.25, it will be about (4 − 6) × 4.5 × 

2.9 m 3 with a volume of ∼60 m 3 . 

The layout and requirements of the gas system for the RICH detector will depend strongly on the chosen 

radiator. In any case, the radiator will be operated at atmospheric pressure. Special care has to be taken to 

keep the radiator clean in particular from dust because of the sensitive mirror and photocathode surfaces, 

and water and oxygen contamination because of their strong absorption of photons with wavelengths 

λ < 190 nm (see fig. 3.39). If the radiator will not be nitrogen, a nitrogen atmosphere would still be 

used during detector shutdown and whenever the working gas mixture is not needed. The working gas 

would come into the vessel through filtering cartridges and cleaning devices. The vessel gas filling rate 

would be about 5-7 m 3 /hour. This high value of the gas flow will be in operation until the concentration 

of oxygen molecules gets to a level where UV photon absorption is sufficiently low. During operation of 

the detector, the gas flow will be decreased to about 2.5 m 3 /hour (corresponds to one total gas exchange 

per day) to constantly renew the gas mixture. These parameters will be monitored carefully by the gas 

control system supplied with devices such as a commercial hydrometer, oxygen-meter and a binary gas 

analyzer. A possible general scheme of the gas system is shown in Fig. 3.40.


He 

CH 4 

N 2 

Figure 3.39: Absorption spectra for water, oxygen and CO2 [71]. 

valves Flowmeters Filter 

Mixer 

Principal scheme of the RICH1 gas system elements. 

gas quality control 

RICH1 

vessel 

to the gas 

collection 

system 

to atmosphere 

Figure 3.40: Scheme of the gas supply system for the RICH detector in the case of operation with a 

40%He+60%CH4 gas mixture.

3.5. Working packages, timelines, costs 83 

3.5 Working packages, timelines, costs 

An outline of the future plans for the development of the RICH detector is presented in table 3.5. 

activity time period 

simulations of the RICH performance 2005 − 2009 

R&D on gas radiator 2005 − 2006 

R&D on WLS film 2005 − 2006 

R&D on photodetectors 2005 − 2009 

R&D on Be-mirror and coating 2005 − 2009 

detector prototype development/ beam test 2006/ 2007 

technical proposal end 2006 

design of the gas system 2007 − 2009 

design of HV supply, cooling system 2007 − 2009 

design of readout electronics, data preprocessing 2007 − 2009 

final detector layout and technology choice 2008 − 2009 

mechanical design 2009 

technical design report 2010 

production, installation, tests 2011 − 2012 

Table 3.5: RICH future planning 

Since at the present stage only a conceptual design for the RICH detector has been developed, a cost 

estimate can only be done with respect to other similar RICH detectors being currently build or planned. 

As a reference point we have taken the RICH2 detector of the LHCb experiment [82], because this 

detector will in several aspects be similar to the RICH detector of CBM. Since for most subsystems of 

the RICH detector several options are still under discussion, only ranges of expected costs can be given. 

Cost for detector R&D are only partially included in the estimate, thus extra costs should be foreseen. 

Item Cost(M) 

mechanics 0.5 − 1 

Be-glass mirrors 1 − 1.5 

photodetector 2.5 − 4.5 

electronics 0.5 − 1 

gas system 0.5− 1 

services ∼ 0.5 

extra R&D ∼ 0.5 

total 6 − 10 

Table 3.6: Cost estimate of the RICH detector.

84 Ring Imaging Cherenkov detector (RICH)

4 Transition Radiation detector (TRD) 


The TRD will provide electron identification and tracking of all charged particles. It has to provide, in 

conjunction with the RICH detector and the electromagnetic calorimeter, sufficient electron identification 

capability for the measurements of charmonium and of low-mass vector mesons. The required pion 

suppression is a factor of about 100 and the required position resolution is of the order of 200-300 µm. 

In order to fulfill these tasks, in the context of the high rates and high particle multiplicities in CBM, a 

careful optimisation of the detector is required. 

Currently, the whole detector is envisaged to be divided into 3 stations, positioned at distances of 4, 6 

and 8 m from the target. A detailed study of the tracking performance in combination with all the CBM 

subdetectors is needed for a final decision on such a segmentation, as well as for the final requirements 

on position resolution within each of the planes. The total thickness of the detector in terms of radiation 

length has to be kept as small as possible to minimise multiple scattering and conversions. The gas 

mixture of the readout detectors has to be based on Xe, to maximize the absorption of transition radiation 

(TR) produced by the radiator. Because of the high rate environment expected in the CBM experiment, 

a fast readout detector has to be used. To ensure the speed and also to minimize possible space charge 

effects expected at high rates, it is clear that the detector has to have a thickness smaller than 1 cm. 

Two solutions exist for such a detector: a multiwire proportional chamber (MWPC) with pad readout 

or a straw tube. Both will be investigated in detail in the following sections as alternative designs. A 

combination of the two is feasible and is a possible solution. 

For the radiator there are two principal possibilities: regular and irregular. The regular radiator, composed 

of foils (polypropylene) and gaps of equal size, is obviously the choice which provides the highest TR 

yield. However, the costs are rather high due to a complicated construction procedure. The irregular 

radiator, composed of fibres and/or foams has a reduced TR yield compared to a regular radiator of 

the same material budget, but the advantage is ease of manufacturing. In addition, such a radiator can 

accommodate the stiff support structure for keeping the required flatness of the MWPC gas window, as 

in the ALICE TRD [86]. The TR performance of various irregular materials (foams and fibres) have 

been evaluated during the prototype tests for ALICE TRD [87]. The final choice of the radiator type in 

CBM TRD will be established after the completion of prototypes tests. 

4.2 An MWPC-based TRD 

The small readout cell required to cope with the high multiplicities for small forward angles in CBM can 

be easily realized with pad readout MWPCs as detector element in TRD. Good performance concerning 

both electron/pion identification [88] and position resolution [89] are established features of such chambers. 

In addition, they are rather easy to manufacture, allow for design flexibility and are very stable over 

a wide range of gas compositions and of gas gain values. 

85


4.2.1 Simulations of electron/pion identification 

A standalone Monte Carlo C++ based simulation code was developed to perform the simulations. The 

main goal is to scan over a reasonable region of the basic parameters of the detector: gas detector thickness, 

radiator thickness, number of detectors and basic properties of the radiator (gap and foil thickness) 

and their influence on the electron/pion separation capability of the TRD. The radiator material 

(polypropylene and air) is kept unchanged for this study. In the simulations, one layer of the TRD consists 

of a radiator, composed of polypropylene foils with air gaps, and a readout chamber filled with 

a Xe/CO2 (85/15) mixture. A mylar foil of 25 µm thickness acts as detector gas barrier. A simulated 

"event" corresponds to either one electron or one pion crossing the detector. 

The following processes are considered in the simulations: i) energy loss of electrons and pions in the 

gas detector, done following the procedure described in [90]. Primary electrons with energies above 10 

keV are treated as delta electrons whose trajectory is collinear with the one of the primary particle; ii) 

for electrons, production and absorption of TR in the radiator, absorption of TR in the mylar foil and 

absorption of TR in the active gas volume. The baseline detector gas thickness is considered here to be 

6 mm. Most of the results shown are for a momentum of 2 GeV/c. 

counts 

N. of produced TR photons per electron per TRD Mean 0.8218 TR spectra 

Mean 8.816 

counts 

4 

10 

3 

10 

2 

10 

10 

1 

RMS 0.904 

0 1 2 3 4 5 6 7 8 9 

N. of photons 

counts 

0.12 

0.1 

0.08 

0.06 

0.04 

0.02 

0 

RMS 6.526 

5 10 15 20 25 30 35 40 45 50 

Energy [keV] 

TR spectra escaped from the detector Mean 12.47 TR spectra absorbed in the detector 

Mean 6.379 

0.045 

0.04 

0.035 

0.03 

0.025 

0.02 

0.015 

0.01 

0.005 

0 

RMS 8.338 

5 10 15 20 25 30 35 40 45 50 

Energy [keV] 

counts 

0.1 

0.08 

0.06 

0.04 

0.02 

0 

RMS 3.128 

5 10 15 20 25 30 35 40 45 50 

Energy [keV] 

Figure 4.1: The distribution of the number of TR photons per electron (upper left panel) and the spectra of TR: 

produced in the radiator (upper right), absorbed in the active detector gas (lower right) and behind the detector 

(lower left). 

Computation of the transition radiation spectra in the radiator is done with a simplified formula [91]: 

dW 

dω = 

4α 

σ(κ + 1) (1 − exp(−Nf σ)) × 

n 

Θn 

1 

ρ1 + Θn 

− 

1 

ρ2 + Θn 

2 

[1 − cos(ρ1 + Θn)] (4.1)

4.2. An MWPC-based TRD 87 

where: 

ρi = ωd1 

2c 

−2 2 

γ + ξi , κ = d2/d1, Θn = 2πn − (ρ1 + κρ2) 

> 0, ξ = ωplasma/ω. (4.2) 

1 + κ 

d1, d2 are the foil thickness and gap width, respectively, and Nf is the number of foils. ωplasma is the 

plasma frequency of the material, ω is the frequency of the emitted photon, σ is the absorption coefficient 

of the radiation in one (foil + gap) layer of the radiator. The X-ray mass attenuation coefficients for 

the radiator, mylar foil and detector gas mixture are taken from [92]. Unless otherwise specified, the 

radiator parameters used for the present simulations are: d1=10 µm, d2=90 µm, Nf =100. The basic TR 

properties for these parameters are shown in Fig. 4.1. It is important to mention that the parameters 

of this equivalent regular radiator were tuned to reproduce the properties of an inhomogeneous radiator 

(polypropylene fiber mats and Rohacell foam) used in the ALICE TRD [88, 93]. The number of foils 

Nf =100 correspond, for the inhomogeneous radiator, to a thickness of about 2 cm. For the study on the 

effect of varying d1 and d2, the results are valid only for a regular radiator. 

[keV] 

12 

11 

10 

9 

8 

7 

6 

5 

produced photons 

absorbed photons 

10 12 14 16 18 20 

foil thickness [ μ m] 

 

1.1 

1 

0.9 

0.8 

0.7 

0.6 

0.5 

0.4 



10 12 14 16 18 20 


Figure 4.2: Dependence of the TR yield on the foil thickness for produced and detected average energy per TR 

photon (left panel) and average number of TR photons (right panel), for a gap width of 90 µm. 

In Fig. 4.2 and 4.3 we illustrate the dependence of the TR yield, produced and detected, on the thickness 

of the foil and of the gap, respectively. The TR yield is quantified by the average energy per TR photon 

and by the average number of the TR photons. Plotted are the yield produced (at the end of the radiator) 

and as detected. With increasing foil thickness, both the average energy of produced TR and the number 

of produced photons increase. However, due to a harder TR spectrum for thicker foils, the number 

of detected photons starts to decrease for a foil thickness larger than 14 µm. Although this effect is 

compensated by the larger TR energy, beyond a foil thickness of about 16 µm the overall gain in TR 

yield is marginal. The dependence of TR on the gap width, Fig. 4.3, is somewhat different: while the 

average energy of the TR is almost constant, the number of the TR photons, both produced and detected, 

is increasing, with a saturation expected for a gap width beyond 300 µm. All of these effects are arising 

due to the formation zone character of TR [94]. 

The calculated spectra of energy deposited in one detector layer are shown in Fig. 4.4 for pions and 

electrons. These spectra are taken as probability distributions to produce a signal with a given energy, 

p(Eπ i ) and p(Ee i ), respectively. The likelihood (to be an electron) for N layers is then computed as


[keV] 

counts 

9 

8.5 

8 

7.5 

7 

6.5 

5 

10 

4 

10 

3 

10 

2 

10 



6 

80 100 120 140 160 180 200 220 240 

gap width [ μ m] 

10 

 

1.2 

1.1 

1 

0.9 

0.8 

0.7 

0.6 

0.5 



0.4 

80 100 120 140 160 180 200 220 240 

gap width [ μ m] 

Figure 4.3: Same as Fig. 4.2, but for the gap width, for a foil thickness of 10 µm. 

el. with TR 

el. without TR 

pion 

0 5 10 15 20 25 30 35 40 45 50 

Energy [keV] 

Figure 4.4: Deposited energy spectra for electrons 

(with and without TR) and pions of 2 GeV/c. 

follows: 

Likelihood = Pe 

, Pe = 

Pe + Pπ 

counts 

10 

6 

5 

10 

4 

10 

3 

10 

pions 

electrons 

90 % 

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 

likelihood 

Figure 4.5: Likelihood distributions for electrons and 

pions. The limit for 90% electron efficiency is indicated. 

N 

p(E e N 

i ), Pπ = p(E π i ) (4.3) 

The likelihood distributions are shown in Fig. 4.5. The pion efficiency (misidentification probability) is 

the relative number of counts in the region corresponding to a certain electron efficiency (90% in our 

case). In the following the term "pion efficiency" is used to quantify the electron/pion identification. The 

inverse of the pion efficiency is the pion suppression factor. For example, a pion efficiency of 1% (pion 

suppression of 100) means that 1% of the pions are misidentified as electrons. In the simulations, we 

require the pion suppression to be 200 or better, to have a safety margin, as in the real experiment high 

rates and high occupancy may lead to a deterioration of the detector performance. The pion rejection 

factor will be slightly improved when taking into account TR propagation and absorption in subsequent 

layers, an effect not considered in the present simulations. 

i=1 

i=1


pion eff. [%] 

0.65 

0.6 

0.55 

0.5 

0.45 

0.4 

0.35 

9 TRDs (180 foils) 


10 11 12 13 14 15 16 


Figure 4.6: Pion efficiency as a function of the foil 

thickness for 9 and 12 TRD layers (d2=90 µm). 

pion eff. [%] 

0.6 

0.5 

0.4 

0.3 

0.2 

0.1 



80 100 120 140 160 180 200 220 

gap thickness [ μ m] 

Figure 4.7: Pion efficiency as a function of the gap 

thickness for 9 and 12 TRD layers (d1=10 µm). 

The dependence of the pion efficiency on the foil thickness and on the gap width is shown in Fig. 4.6 

and Fig. 4.7, respectively. The results for a TRD configuration with 9 and 12 layers are shown. In the 

case of a 9 layer TRD, the number of foils has to be increased to Nf =180 in order to obtain the same 

pion rejection as for 12 layers with Nf =100. Fig. 4.6 shows that increasing the thickness of the foils 

from 10 to 16 µm improves the pion rejection by a factor of 2. The increase of the material budget due 

to the increase of the foil thickness will obviously have a negative impact on the tracking capability of 

the TRD. This needs to be evaluated in separate studies. Fig. 4.7 shows that an increase of the gap width 

from 90 to 210 µm leads to an improvement of the pion rejection by a factor of 6. As increasing the gap 

does not imply any negative impact on the material budget, it is clear that a regular radiator with a large 

foil gap would lead to a significantly better electron/pion identification than an irregular radiator (which 

corresponds to small gap values) of the same material budget. 

In Fig. 4.8, left panel, the pion efficiency as a function of number of layers is shown for different radiator 

thicknesses (number of foils). Also shown is the pion efficiency computed without radiator (only from 

dE/dx in case of electrons). The right panel of Fig. 4.8 shows a similar dependence of the pion efficiency 

on the thickness of the gas readout chamber. The required pion efficiency can be reached with different 

detector configurations. A lighter radiator (which is more favorable for tracking) implies more layers (at 

higher costs for the extra electronics channels). A thicker readout chamber implies the benefit of fewer 

layers, but needs to be tested with prototypes concerning the rate capability and the position resolution 

performance. The decision on the final configuration has to await the final assessments on the required 

position resolution performance. 

In Fig. 4.9 we present the dependence of the pion efficiency on the CO2 concentration in the detector 

mixture, for two radiator configurations and a 9 layer TRD. As expected, the pion rejection shows a 

strong dependence on the CO2 content, arising from the efficiency of TR absorption in the active detector 

gas. For instance, about a factor of 2 better rejection is achieved for 10% CO2 compared to 30% CO2. 

The amount of CO2 is dictated by detector consideration (stability, gas gain) and we expect to be able 

to operate our detectors at a CO2 content of 10-15%. Another point to emphasize from Fig. 4.9 is the 

importance to have a regular radiator, for which one can choose the parameters more easily than for an 

irregular one. This is in particular the case for the gap width, which does bring a nice improvement of the 

pion rejection, essentially without any penalty. For the two configurations (d1/d2/Nf ) that we investigate, 

a factor of 2 better rejection is achieved with the large gap width variant (d2=210 µm) compared to the


pion efficiency [%] 

10 

1 

-1 

10 

NO radiator 

80 foils 

100 foils 

120 foils 

140 foils 

160 foils 

180 foils 

200 foils 

8 9 10 11 12 

No. of TRD 


10 

1 

-1 

10 

3 mm 

4 mm 

5 mm 

6 mm 

7 mm 

8 mm 

9 mm 

10 mm 

8 9 10 11 12 

No. of TRD 

Figure 4.8: Left panel: Pion efficiency as a function of number of the TRD layers for different number of foils. 

The pion efficiency based only on dEdx (no TR included) is also shown. The detector thickness is 6 mm. Right 

panel: pion efficiency as a function of number of the TRD layers for different detector gas thicknesses, for Nf =100. 


1.2 9 TRD - 10/90/180 

1 

0.8 

0.6 

0.4 

0.2 

9 TRD - 15/210/100 

0.1 0.15 0.2 0.25 0.3 0.35 

CO2 content [%] 

Figure 4.9: Pion efficiency as a function CO2 concentration in the detector mixture for two radiator configurations 

(d1/d2/Nf ), for a 9 layer TRD. 

case of small gap width (d2=90 µm), even at a smaller material budget. Note that the case of small gap 

width does correspond to an irregular radiator that would have anyway more material that its equivalent 

regular radiator parametrization. 

In summary, the results of our simulations demonstrate that a TRD with 9 to 12 layers can fulfill the 

required electron/pion identification performance in CBM. From the multi-dimensional parameter space 

of the detector which we have explored, a configuration will be chosen based on further studies with 

simulations and detector prototypes.


4.2.2 Tests with prototypes 

Four prototype MWPCs have been tested with sources ( 55 Fe) and beams. In addition, one GEM-readout 

chamber was studied in the beam measurements for reference. The two different layouts used for the 

MWPCs are sketched in Fig. 4.10. Three different prototypes were realized for the design presented in 

the left panel of Fig. 4.10, differing in anode pitch: 2 and 4 mm were used for the two chambers built 

at GSI (labelled GSI1 and GSI2 in the following plots), while 2.5 mm was used for the chamber built in 

Bucharest. The anode-cathode gap for these three chambers is 3 mm. The entrance window of 25 µm 

aluminized kapton simultaneously serves as gas barrier and cathode plane. The pad planes consist of a 

segmented cathode with 8 pads with an area of 6 cm 2 each. The width of one pad is 7.5 mm, the length 

80 mm. 

z 

x 

3 mm 

3 mm 

anode 

wires 

cathode pads 

Radiator 

particle 

amplification 

region 

entrance 

window 

Figure 4.10: Layout of the MWPC prototypes. Left panel: chambers built at GSI and in Bucharest, right panel: 

chamber built in Dubna. 

Another chamber, built in Dubna, has a different design, with a drift region of 8 mm and an amplification 

region of 4 mm, as seen in the right panel of Fig. 4.10. The anode wire pitch is in this case 2 mm, while 

the cathode wires are spaced by 0.5 mm. The size of the readout pads is 3×4 mm 2 . For all chambers, 

the anode wires are made of gold-plated tungsten and have a diameter of 20 µm. 

In Fig. 4.11 the layout of the GEM chamber from the Dubna group is shown. The detector has a drift 

gap of 3 mm and 3 amplification stages. The size of the readout pads is 10 x 20 mm 2 . 

A charge-sensitive preamplifier/shaper (PASA), built with discrete components for the tests of ALICE 

TRD prototypes was used for the Bucharest prototype. It has a gain of 2 mV/fC and noise of 1800 

electrons r.m.s. For both MWPCs from GSI, ASIC PASAs, also from ALICE TRD [86], with a gain of 6 

mV/fC and a noise of 1000 electrons r.m.s were used. The FWHM of the output pulse is, for both PASA 

types, about 100 ns for an input step function. An octal channel PASA was designed for the Dubna 

MWPC and GEM, based on the KATOD-1 ASIC [95]. A low-noise preamplifier ASIC PU-1/2 [96] was 

used as a first stage. The total gain is 10 mV/fC, with fully differential output. The level of noise was 

about 1500 e (FWHM). Two different shaping time values were used for GEM and MWPC, 400 ns and 

700ns, respectively. For the readout of the chambers we used an 8-bit nonlinear Flash ADC (FADC) 

system with 33 MHz sampling frequency, 0.6 V voltage swing and an adjustable baseline. 

The prototypes have been tested with the Ar- and Xe-based gas mixtures (with 15% and 30% CO2 as 

quencher) using a 55 Fe X-ray source of 5.9 keV and beams. Tests with X-rays at high-rate are under way.


4.2.2.1 Tests with 55 Fe source 

Figure 4.11: Geometry of the GEM detector. 

In Fig. 4.12 detector signals for two gas mixtures are shown, which were obtained with the 55 Fe source 

for chamber GSI2. The anode voltage was 1.35 kV for the Ar-based gas mixture and 1.55 kV for the 

Xe-based gas mixture. These signals are from the pad on which the collimated source was centered. The 

shape of the signals is a convolution of the detector signal and the PASA response. The longer tails in 

case of the Xe-based mixture is the result of the slower ion motion. Note that the mobility of the Xe ions 

is almost 3 times lower than that of Ar ions [97]. 

Figure 4.12: Time development of the signals for the GSI chamber with 4 mm anode pitch measured with a 55 Fe 

source. On the left side for measurements with an Ar based gas mixture, on the right side for a Xe based mixture. 

In both cases the fraction of CO2 was 15%.


The energy spectrum of the 55 Fe source is obtained by integrating the signals of the main pad and one 

neighbouring pad on each side. This is done to account for the charge sharing between adjacent pads. In 

Fig. 4.13 we present the energy spectra of 55 Fe measured for the Ar- and Xe-based gas mixtures with a 

CO2 fraction of 15 %. The anode voltage was 1.35 kV (Ar,CO2) and 1.55 kV (Xe,CO2). Beside the main 

peak corresponding to the full energy deposit of 5.9 keV, the escape peak corresponding to the partial 

energy deposit of 2.9 keV in the Ar-based and around 1.2 keV in the Xe-based gas mixture is clearly 

visible for both cases. 

Figure 4.13: The spectra of 55 Fe measured with Ar,CO2(15%) (left panel) and Xe,CO2(15%) (right panel )for 

the GSI chamber with 4 mm anode pitch. The curves are the results of gaussian fits to the main peak. 

The curves are the results of gaussian fits to the main peak, to extract the mean value and the width of the 

energy spectrum. The achieved resolutions are 10-11% for the Xe-based and 11-14% for the Ar-based 

gas mixture. 

In Fig. 4.14 the energy spectrum of 55 Fe measured for Ar/CO2(85/15), obtained with the MWPC built in 

Bucharest is shown. The energy resolution is 8.6%. 

The gain as a function of the anode voltage Ua is measured using a 55 Fe source placed in front of the 

entrance window of the chamber. Information about gas gain curves are important inputs for programs 

simulating the detector behaviour under high rate conditions, when space charge effects lead to an decrease 

of the detector signal. The gain was measured for both GSI chambers as a function of the anode 

voltage Ua for different gas mixtures. The results of the measurements for the Xe,CO2(15%) mixture 

are shown in figure 4.15. The lines in this figure are results from calculations [98] with the program 

Magboltz [99]. These calculations are reproducing the measurements very well, giving confidence for 

future simulations which will be employed for detector design. 

4.2.2.2 Beam tests 

The aim of the measurements performed at the secondary beam at SIS/GSI was to test the general detector 

performance of the different readout chambers under high rate conditions. The bulk of the measurements 

were performed with a mixed proton and pion beam at 2 GeV/c for two Xe-based gas mixtures: 

Xe,CO2(15%) and Xe,CO2(30%). 

The setup used for the beam tests, sketched in Fig. 4.16, is composed of the following detectors: 

• two scintillator counters (S0, S1), used for beam definition and trigger, with area 5×5 cm 2 .


Figure 4.14: Energy spectra of 55 Fe measured with the 

MWPC built in Bucharest. 

S0 

Si1 Si2 

GSI1 GSI2 

Gain 

10 4 

10 3 

Bukarest GEM MWPC 

4 mm 

2 mm 

Xe,CO 2 (15%) 

lines: simulations, NIM A523 (2004) 302 

1.4 1.5 1.6 1.7 1.8 1.9 2 

Ua (kV) 

Figure 4.15: Gain dependence as a function on the 

anode voltage for the two GSI chambers (2 and 4 mm 

anode pitch), for the Xe,CO2(15%) mixture. The lines 

are results of calculations with the program Magboltz 

[99]. 

Figure 4.16: Sketch of the setup used for the beam tests (not to scale). The different components are explained 

in the text. 

• two MWPCs built at GSI (GSI1, GSI2) 

• a MWPC built by the Bucharest group 

• a MWPC readout chamber built by the Dubna group 

• a GEM with pad readout, also built in Dubna 

• two silicon strip detectors (Si1, Si2) with active area of 32 x 32 mm 2 . Each have strips of 50 µm 

pitch in both x and y direction. 

• a Pb-glass calorimeter (Pb) with dimensions of 6×10 cm 2 and a depth of 25 cm (equivalent to 

10 X0) for electron identification. This detector was only inside the experimental setup for a run 

dedicated to electron identification. 

The data acquisition (DAQ) was based on a VME event builder and developed at GSI Darmstadt [100]. 

The beam trigger was defined by a coincidence of the two scintillator counters. The size of the beam 

was about 10 cm 2 FWHM for all measurements. The measurements have been carried out with positive 

secondaries of 1 and 2 GeV/c momentum. The composition of the beam varies as function of momentum, 

which can be seen in the time-of-flight (ToF) spectra shown in Fig. 4.17, measured with the two 

S1 

Pb 

Beam


10 4 

10 3 

10 2 

10 

1 

p=1 GeV/c 

750 1000 1250 1500 1750 

10 4 

10 3 

10 2 

10 

1 

p=2 GeV/c 

750 1000 1250 1500 1750 

ToF (a.u.) 

Figure 4.17: Time-of-flight spectra for 1 and 2 GeV/c beam momentum. 

scintillators. At 1 GeV/c, the mixture of pions and protons makes the study of the separation between 

these two species using dE/dx a suitable substitute for the electron/pion case, for which the rates were 

not high enough. However, the beam intensities at 1 GeV/c were also not sufficiently high to study the 

relative rate performance of pions and protons. We have resorted to the 2 GeV/c beam, for which the 

beam almost entirely consists of protons, but enabled us to do basic exploratory measurements of detector 

performance as a function of rate. The rate was chosen by varying the extraction time of the primary 

beam from 2 to 0.2 seconds. 

The time dependence of the average pulse height is shown in Fig. 4.18 for the gas mixture 

Xe,CO2(70%/30%). From left to right the spill length is decreasing (increasing rate). From top to 

bottom the data for the different readout chambers are shown. The time zero has been shifted arbitrarily 

by about 0.2 µs to have a measurement of the baseline. One can notice that the absolute magnitude of 

the peak is changing very little with increasing rate. However, the tails of the signals are larger with 

increasing beam rate, probably due to pile-up. 

The signals illustrated in Fig. 4.18 are summed over adjacent pads and integrated around the peak values 

to produce the energy deposit spectra. The resulting spectra are shown in Fig. 4.19 for all five chambers. 

The lines represent Landau fits to the distributions. The distributions are characterised by the most 

probable value (MPV) and the widths. 

In Fig. 4.20 we present the results for the Dubna detectors. Both the MWPC and the GEM chambers 

show no degradation of the signal amplitudes for high rates. The results for the relative detector gain 

(derived from signal amplitudes) as a function of the rate for the GSI detectors are shown in Fig. 4.21. 

These results correspond to the gas mixture Xe,CO2(15%) with anode voltages of Ua=2.0 kV and 1.55 

kV for GSI1 and GSI2, respectively. The gain values at low rate for these settings are 11300 for GSI1 

and 7500 for GSI2 (see Fig. 4.15). The lines are simulations taking into account the geometry of the 

MWPC [101, 99] and the increasing space charge with increasing rate [102]. Again, the measurements 

show no decrease of the gain with increasing rate, while the simulations indicate a gain drop of a few 

percent, depending on the anode wire pitch. 

The dependence of the width of the charge distributions on rate for the gas mixture Xe,CO2(15%) is


(mV/0.74) 

50 

2 

S1:260000, S2:150000 /spill; Xe,CO 2 (30%) 

50 

1 

0 

0 

0 

0 

0 0.5 1 1.5 0 0.5 1 1.5 0 0.5 1 1.5 0 0.5 1 1.5 

50 

50 

50 

50 

0 

0 0.5 1 1.5 0 

0 0.5 1 1.5 0 

0 0.5 1 1.5 0 

0 0.5 1 1.5 

50 

50 

50 

0 

0 0.5 1 1.5 0 

0 0.5 1 1.5 0 

0 0.5 1 1.5 0 

0 0.5 1 1.5 

10 

0 

0 0.5 1 1.5 0 

0 0.5 1 1.5 0 

0 0.5 1 1.5 0 

0 0.5 1 1.5 

20 

10 

10 

20 

10 

20 

0 

0 0.5 1 1.5 0 

0 0.5 1 1.5 0 

0 0.5 1 1.5 0 

0 0.5 1 1.5 

0.5 

50 

50 

50 

25 

10 

20 

0.2 

Drift time (μs) 

Figure 4.18: Average pulse height as a function of time for the different readout chambers (rows) and for 

different spill lengths (columns, from left to right corresponding to 2, 1, 0.5 and 0.2 s extraction time). 

shown in Fig. 4.22. One can see a rate effect: with increasing rate the width is increasing. It is not 

easy to quantify to which extent this broadening is an effect of rate (due to build up of space charge) 

or of pile-up. It is clear that a broadening of the charge distribution would lead to a degradation in 

electron/pion identification performance. After this broadening is thoroughly established, its influence 

on the identification power can be quantitatively assessed via simulations. This work is under way. 

The effects of constant or slight increasing average signal and of the broadening charge distributions 

with increasing rate seen in the data are probably effects of event pile-up. This effect can be reduced 

with tighter cuts for the event selection and is also foreseen to be improved in future measurements by 

changes at the trigger level. Also, a preamplifier/shaper with a shorter shaping time will be used in the 

future. 

Another important aspect is the rate dependence of the position resolution. The position is reconstructed 

in our chambers via charge sharing among adjacent pads. The pad response function (PRF) is a measure 

of charge sharing. The PRF measured in the beam is presented in Fig. 4.23 for 2 and 4 mm anode wire 

pitch. Shown is the ratio of the charge (integrated over a gate of 0.6 µs around the signal maximum) 

on the central pad to the sum of the charges on the central pad and the adjacent pads as function of the 

position of the hit. This position was determined with the Si-strip detectors. 

The rate dependence of position resolution performance is presented in Fig. 4.24, derived from the two 

GSI1 


Buc 

Dub 

GEM


6 tb Integral 

300 

250 

200 

150 

100 

50 

6 tb Integral 

Ch1 

Entries 40110 

Mean 762.6 

RMS 

2 

χ / ndf 

Constant 

816.7 

1156 / 1077 

1680 ± 13.3 

MPV 329.6 ± 1.4 

Sigma 118.7 ± 0.8 

0 

0 1000 2000 3000 4000 5000 

300 

250 

200 

150 

100 

50 

6 tb Integral 

Ch2 

Entries 41963 

Mean 846.1 

RMS 852.6 

2 

χ / ndf 

1189 / 1100 

Constant 1564 ± 11.9 

MPV 384.6 ± 1.5 

Sigma 133.5 ± 0.8 

0 

0 1000 2000 3000 4000 5000 

400 

350 

300 

250 

200 

150 

100 

50 

Ch3 

Entries 42759 

Mean 657 

RMS 

2 

χ / ndf 

791 

1020 / 1074 

Constant 2117 ± 16.4 

MPV 256.4 ± 1.2 

Sigma 101.7 ± 0.7 

0 

0 1000 2000 3000 4000 5000 

Figure 4.19: Total energy deposited in the chambers. Left panels, top to bottom: GSI1, GSI2, and Bucharest 

chambers. Right panels: MWPC and GEM chambers from Dubna. The lines are Landau fits. 

Figure 4.20: Rate dependence of the average signal for the MWPC (left panel) and GEM (right panel) detectors 

built in Dubna. 

GSI chambers data (assuming that both have the same resolution). Resolutions of 300 µm are achieved 

at low rates, although the geometry of the present detector was not optimized for position resolution. 

A steady degradation of the resolution is observed for higher rates, amounting to about 30 µm at 100 

kHz/cm 2 , which is rather tolerable. 

In summary, the first exploratory beam measurements at high rates indicate that the required performance 

for the CBM TRD can be achieved with MWPCs with pad readout. More quantitative statements will be 

derived from further analysis of the existing data, as well as from new measurements, which we foresee


0 

G/G 

1.04 

1.02 

1 

0.98 

0.96 

0.94 

0.92 

0.9 

0.88 

Data: 

MPV extracted 

from landau fits 

G (2000V) = 11300 

0 

G (1550V) = 7470 

0 

Data, GSI 1, 2mm pitch 

Data, GSI 2, 4mm pitch 

Simulation, 2mm pitch 

Simulation, 4mm pitch 

10 4 

10 5 

10 

2 

6 

rate [Hz/cm ] 

Figure 4.21: Most probable value of total deposited 

energy as a function of rate for the two GSI chambers. 

The lines are simulations. 

Charge ratio 

0.8 

0.7 

0.6 

0.5 

0.4 

0.3 

0.2 

0.1 

σ = 3.51 mm 

exp 

2 mm pitch 

−8 −6 −4 −2 0 2 4 6 8 

Position (mm) 

Charge ratio 

Width, σ (%) 

150 

140 

130 

120 

110 

100 

90 

80 

70 

10 4 



Bucharest 

10 5 

Rate (Hz/cm 2 ) 

Figure 4.22: Width of the total deposited energy spectrum 

as a function of rate. 

0.8 

0.7 

0.6 

0.5 

0.4 

0.3 

0.2 

0.1 

σ = 3.66 mm 

exp 

4 mm pitch 

−8 −6 −4 −2 0 2 4 6 8 

Position (mm) 

Figure 4.23: Pad response function for the GSI chambers with 2 and 4 mm anode pitch. 

to realize in the course of the next years, both with beams and with x-ray generators. Nevertheless, we 

can conclude at this moment that the detector configurations which we have investigated are suited for 

usage in the CBM TRD. 

4.2.3 Gas system 

The TRD uses xenon as the main counting gas. Excluding organic quenchers due to their flammability 

and CF4 due to its potential aging risk, the quencher has to be carbon dioxyde. The CO2 concentration


Resolution (μm) 

450 

400 

350 

300 

250 

10 4 

10 5 

Rate (Hz/cm 2 ) 

Figure 4.24: Position resolution as a function of rate for the GSI chambers. 

may be chosen over a range between 10 % and 30 %, depending on performance issues. The choice of 

a high price (5 /l) and high density (5.58 kg/m 3 ) noble gas determines the design of the gas circulation 

system, in particular for use with large size and thin multi-wire proportional chambers (MWPC). Assuming 

a segmentation of each detector plane into four quadrants, the largest chambers would be 2.3 m 

long. The total detector volume adds up to 3 m 3 . The main implications of these numbers are that: i) 

The full system must be as gas-tight as possible, in order to limit the cost of gas lost through leaks. A 

reasonable leak rate during operation is 10 % of the total detector volume per year. ii) The pressure at 

the detectors must be accurately regulated at a value only slightly above the ambient pressure, in order to 

limit the deformation of the chamber enclosing cathodes and thus keeping the gas thickness and gas gain 

as uniform as possible over the entire active area. With nowadays instruments, overpressures of 1 mbar, 

with an accuracy better than 0.1 mbar, are routinely achieved. 

The proposed gas system for a 3-station MWPC-based TRD is schematically shown in Fig. 4.25. It is 

a closed-loop circulation system. Initially, the detectors are flushed with CO2 from the gas supply until 

all air is vented away. At this point, xenon injection starts, and the returning gas is passed through a 

membrane separator which keeps the xenon in the loop but lets the CO2 diffuse through its walls. In 

this way, the amount of xenon lost for the filling of the detector is limited to the small loss through the 

membrane. Once the desired gas composition is reached, the system is switched to run mode. In order 

to save gas, fresh gas injection could be activated only to compensate for the gas lost through leaks. This 

is automatically done by measuring the pressure at the high pressure buffer, after the compressor, since 

the amount of gas in the rest of the system is kept constant. 

The detector pressure regulation is done in two steps: a pressure sensor located at the outlet of each 

station -or height level- ensures that the detector overpressure is kept at the desired value by regulating 

the flow through the chambers. A second pressure sensor located at the compressor module regulates 

the total flow through the compressor. The gas is continuously filtered through a copper catalyst which 

removes O2 and H2O, and the gas quality is monitored with appropriate analysis instruments. The filters


Gas Room Near Cave Cave 

Exhaust 

(N ) 

2 

Xe recovery 

Exhaust 

(CO ) 

2 

Gas 

Supply 

Cryogenic 

Xe Plant 

Membrane 

Separator 

Filling/Purging 

Purifier 

Mixer 

Run 

Circu 

lation 

Buffer 

Distribution 

Module 

Analysis 

Compressor 

Module 

Figure 4.25: Layout of the gas system. 

Detectors 

themselves need eventually to be regenerated with a flammable (Ar-H2) mixture at 200 ◦ C. This module 

is therefore installed in the gas room. In this approach, N2, not removed by any filter, slowly builds up in 

the mixture during each running period. It has been shown, however, that concentrations of order 10% 

of N2 in a Xe-CO2 mixture are tolerable, since the impact on the gas gain is small [98]. 

The precious xenon may eventually be recovered at the end of each run. To do so, CO2-free gas is dumped 

into a cryogenic plant where the xenon is frozen and the remaining gaseous N2 is evacuated. The clean 

noble gas is thereafter pumped into the primary supply vessels, ready to be used the next running period. 

The system is PLC-controlled. A user interface provides access to monitoring information and activation 

of operating modes. A suitable package enables the interfacing to the general Detector Control System 

of the experiment. A set of alarms and interlocks can stop the gas system, and other systems such as high 

voltage, in case unsafe pressure or gas conditions are detected. 

4.2.4 Readout electronics 

The detector signals of the MWPC-based TRD are amplified by a preamplifier/shaper (PASA). The total 

number of channels is above half a million, depending on the number of layers considered. It is obvious 

that, for such a high number of channels, reasonable costs for front-end electronics (FEE) can be achieved 

only through custom ASIC developments. Research and development for the PASA of the TRD has two 

challenges: i) low noise with short shaping time and high pulse rate. ii) low power consumption and small 

chip size. The two issues are decoupled. First, one can check with a prototype that the requirements can 

be fulfilled using standard technology (0.35 µm) and that tests with prototype chambers give the expected 

physics performance. In a second step one needs to develop a completely new prototype in state-of-theart 

technology with close to final specifications (for example 0.18 or 0.13 µm). The basic requirements 

for a PASA chip suitable for the CBM TRD are given in Table 4.1. 

In order to achieve an overall noise of less than 1000 electrons per channel for a typical input capacitance 

of 15-35 pF, the use of a low-noise circuit is required. A well proven topology to fulfill such a requirement 

is shown in Fig. 4.26. 

Each channel consists of a low noise charge-sensitive amplifier and an active CR-RC pulse shaper and 

a source follower buffer that can handle both polarities. The main noise contributor in this scheme is 

the input transistor of the preamplifier that is based on a folded cascode topology. The cascode consists 

P 

P 

P


Parameter specification simulated 

Noise rms (Cinp=35 pF) 1000e 850 e 

Shaping time 70 ns 75 ns 

Gain 10mV/fC 40 mV/fC 

Table 4.1: The requirements for the preamplifier/shaper chip for the TRD. 

Adaptiv−bias 

From Detector 

Cd 

MF 

Preamplifier 

MPZ 

Cf Vref 

C2 

CPZ 

Preamplifier Vref1 

R 

R1 R2 

Amplifier 

Non−Inverting stage 

Figure 4.26: Schematics of the fast PASA done in 0.35µm technology 

of a common-source stage followed by a common-gate stage. It combines two transistors (a wide input 

transistor and a narrower cascode transistor) to obtain: i) high transconductance and low noise of a 

wide transistor, ii) high output resistance and low output capacitance of a narrow transistor, and iii) 

reduced capacitance between output and input. In addition, a pole-zero network is included to avoid the 

undershoot which strongly limits the counting rate behavior. In case of a second pulse arriving during 

the period of undershoot, it will be superimposed on the undershoot and an error will be introduced in 

the pulse amplitude. In the design, a MOS transistor (MF) is used with a feedback capacitor C f that 

is continously discharged with a decay time td = D f *Rds(MF). This continuously sensitive design is 

particularly suitable for a detector with high occupancy and high counting rate. In order to increase 

the gain and shape of the signal a shaper is built around a Miller OTA (Operational Transconductance 

Amplifier). By means of the external reference voltage Vre f , this part has in addition the capability of 

maximizing the dynamic range for a given polarity. The reference voltage is set closer to the positive 

rail such that the output swings towards the negative rail for positive input charges. For negative input 

charges the reference voltage is set closer to the negative rail such that its output swings toward the 

positive rail. 

A first prototype using this design was submitted in October 2004 and has just been received back from 

the foundry. First tests have already been undertaken. Since the design was targeted for a slightly 

different type of detector the conversion gain is slightly higher (40mV/fC) in this prototype version 

compared to the requirements for the TRD. This can easily be lowered by adding a resistor of suitable 

size in series to the external reference voltage Vre f . 

In this first prototype the peaking time is 30 ns (t0/100), and the FWHM is about 75 ns. It returns to 

the baseline after 270 ns. In terms of noise this circuit fulfills the requirement with the ENC being less 

than 850 electrons for an input capacitance of 35 pF. In terms of power consumption the circuit uses 

8.5 mW/channel. The chip is fabricated in 0.35 µm standard CMOS technology, and has a pad pitch of 

50 µm. 

The next step in the analog front-end electronics development will be to migrate from 0.35 µm technology 

to a smaller technology. This should yield a reduction in power consumption. A prototype development 

of a new circuit in 0.13 µm IBM technology is already under way. The proposed schematic layout is 

Vout


shown in Fig. 4.27. 


From Detector 

Selectable gain 

Preamplifier 

Select Select 

Select 

Select 

Pole−Zero 

Network 


Selectable shaping 

and gain stage 

n−th−Order filter 

Amplifier 

Figure 4.27: Schematics of the fast PASA under development in 0.13µm technology. 

This circuit consists of a low-noise selectable-gain preamplifier circuit that can handle both polarities. 

The choice of a gain range in the preamplifier is made by switching the relevant range capacitors. The 

reason of choosing a selectable gain preamplifier is to provide a versatile preamplifier potentially suitable 

for several of the proposed CBM detectors (RICH, ECAL and TRD). The core preamplifier is again based 

on the same topology as used in ALICE-TPC/TRD. The change in decay time (due to changes in the gain 

range) also influences the pole-zero network, which has to change to preserve the same relation to avoid 

undershoot. The shaper/gain circuit is also made selectable. The purpose of the selectable shaper/gain 

circuit is to amplify the input signal to match the full scale voltage of the ADC ensuring that maximum 

signal resolution is achieved. Several programmable shaper/gain topologies are under investigation to 

identify the best candidate for the proposed detectors. 

4.2.5 Design considerations, mechanics, and material budget 

Our simulations have demonstrated that the required pion rejection factor of 100 can be achieved with a 

TRD of 9 to 12 layers, depending on the radiator thickness, for a detector thickness of 6 mm. The final 

configuration concerning the radiator thickness, number of layers and their spatial separation, will be 

chosen according to the required position resolution, which will be derived from simulations, based on 

tracking/matching performance. The choice to use either a regular or an irregular radiator (a combination 

of the two is also conceivable) is left open for the moment. Another important parameter contributing to 

electron identification is obviously the active detector gas thickness. Constraints on this will be derived 

from high rate studies as well as from measurements on position resolution performance. 

Element Material X/X0[%] 

radiator foils(fibres)/gas 0.20-0.50 

entrance foil/stiffener alluminized kapton 0.06 

drift chamber gas Xe/CO2 0.04 

pad plane G10/Cu 0.15 

readout motherboards G10/Cu 0.90 

1 TRD layer 1.35-1.65 

Table 4.2: Material budget of one TRD module. Only components contributing within the detector’s active area 

are listed. 

In Table 4.2 we summarize the radiation length of all components in the active area of the detector for 

Vout

4.3. A straw-based TRD 103 

one layer. Given the above arguments, for the radiator we have defined a range of values. Note that, 

for the entrance foil, the reinforcement required to keep it flat is included in its material budget. For 

the readout chamber gas, Xe,CO2 with a total thickness of 6 mm has been considered. The pad plane is 

included in the calculation with a thickness of 250 µm and a copper coating of 5 µm. We have considered 

a readout motherboard of 800 µm thickness and with 6 layers of copper of 17 µm thickness, with Cu 

coverage of 50% per layer. For the whole TRD, the total radiation length in the active area will be of 

the order of 15%. The material budget of the detector frames (which can amount up to about 10% of the 

active area) and its effect on the production of secondary particles has not been estimated yet. It is clear 

that, in order to minimize the background due to secondaries, the chamber frames have to be positioned 

in a projective geometry. 

Concerning the sizes of the individual chambers, it is too early to make final statements. We know from 

experience with the ALICE TRD production that chambers with sizes up to 1.2×1.6 m 2 can be built with 

the uniformity of the wire-cathode distance better than 100 µm. In fact, all the infrastructure to build such 

chambers can be inherited from the four production sites of the ALICE TRD. At this stage we can also 

not decide whether the detector modules within a layer will be positioned in a vertical plane or will have 

angles to favor normal incidence of the tracks. Position resolution studies will be performed in order to 

answer this question. 

Also relying on position resolution performance, in particular on tracking through the TRD, is the question 

of stereo angles for the pad direction in successive layers. Since the readout pads will have to be 

rather long, good position resolution is obtained within one layer only in one coordinate (wire direction). 

Optimizations on the stereo angle will be performed to ensure the best tracking performance of the TRD. 

4.2.6 Costs 

The cost estimate can only be done at this stage using a scaling procedure with respect to other similar 

detectors. A case in point is the ALICE TRD [86], which we have used to estimate the costs of the 

TRD in CBM. However, due to unknown choice of implementation (both for radiator and detectors, 

for example), as well as due to not obvious extrapolations for the FEE costs, we are giving ranges of 

expected costs, see Table 4.3. Part of the uncertainty on the number of layers needed is included in the 

given cost ranges. If needed, we keep the option to compromise on occupancy and build more layers 

within the same budget. The values in Table 4.3 include the costs of R&D activities. 

Item Cost (k) 

Radiator 200-700 

Readout chambers 2000-2600 

FEE 3100-3600 

Services (HV/LV, cooling) 1200-1500 

Gas system 400-500 

Support frames 800-1000 

Total 7900-9900 

4.3 A straw-based TRD 

Table 4.3: The costs of the main components of the MWPC-based TRD. 

A possible alternative to the MWPC-based TRD is a TRD based on straw tubes. Such a Transition 

Radiation Tracker (TRT) was proposed many years ago for collider experiments [103] and is now built


for the ATLAS experiment at LHC [104]. Straws were chosen as a detecting element as they provide 

a high modularity for the detector in areas with different irradiations levels. Due to the fixed target 

geometry the angular distribution of particles is peaked at small angles, hence the segmentation with 

very different readout chamber sizes is required. For example, at a distance of 4 m from the target, we 

expect particle rates of about 100 kHz/cm 2 at small angles for 10 MHz minimum bias Au+Au collisions 

at 25 AGeV. The technical task is to develop for such rates a detector for track reconstruction with a 

resolution better than 200 µm, and efficient pion rejection by more than two orders of magnitude. 

4.3.1 Monte Carlo simulation 

A stand-alone Monte Carlo simulation program has been used to estimate the rejection power of the 

proposed TRT detector and to optimize the detector parameters. The program was primarily developed 

for the ATLAS TRT and is based on Transition Radiation (TR) simulation in the so called field transport 

approach [105], on dE/dx energy loss simulation using PAI [106] model, and on GEANT3 [107] for 

geometry description and transport of particles. The model has been carefully checked by comparison to 

ATLAS test beam data, and has shown an agreement within a few percent accuracy for different particles, 

energies, detector parameters and working conditions [108]. Even such a tiny effect as the TR production 

on the straw walls can be described by the model. 

For simulations it is supposed that a TRT module (Fig. 4.28) consists of six identical layers with radiator 

foils and adjacent straw tubes. Straws are shifted from layer to layer by the straw radius to minimize the 

fluctuations in the particle path in the straw gas at different impact points. A single straw is described in 

this model as a cylindrical volume of a Kapton tube filled with gas and a tungsten wire along the cylinder 

axis. The radiator is described as a set of regularly spaced polypropylene films. 

Radiators 

Straws 

Particle 

Figure 4.28: Schematic view of one TRT module. 

The whole TRT detector consists of three modules. A primary single particle crosses all the 18 layers 

at normal incident direction and with spread of position across the straw radius. The following detector 

parameters have been fixed in the simulation: 

• straw wall: Kapton, thickness: 60 µm 

• anode wire: tungsten, 30 µm in diameter 

• gas mixture: 70% Xe, 20% CF4 and 10% CO2 at atmospheric pressure 

• radiator: polypropylen 

The straw diameter, the total thickness of one radiator, the thickness of the radiator foil, and the distance 

between two foils were the subjects to optimization. Fig. 4.29 shows the energy loss spectra for pions and


electrons in a single straw. The energy deposition of electrons clearly exhibits an additional contribution 

due to the absorbed TR photons. The pion spectra are identical for all straws. The electron spectra 

are slightly varying in different layers due to the TR accumulation along the particle path: TR photons, 

generated in the first radiator, not obligatory to be absorbed in a straw of the first layer, but can pass 

through and be absorbed in the second straw layer or even later. For the considered detector configuration 

the mean energy loss in a single straw is ≈2.2 keV for pions and 5.3-6.9 keV for electrons. 

3500 

3000 

2500 

2000 

1500 

1000 

500 

0 

0 10 20 30 

Energy loss in straw (keV) 

700 

600 

500 

400 

300 

200 

100 

0 

Pion and electron spectra 

Pion, first layer 

Electron, first layer 

0 10 20 30 


3500 

3000 

2500 

2000 

1500 

1000 

500 

Pion, sixth layer 

0 

0 10 20 30 


600 

500 

400 

300 

200 

100 

Electron, sixth layer 

0 

0 10 20 30 


Figure 4.29: Differential spectra of energy deposition in a single straw for pions (top) and electrons (bottom). 

The histograms on the left side correspond to the first straw layer, on the right – to the sixth layer. The result is 

obtained for 20 GeV primary particles, 4 mm straws, 2.6 cm radiator with 15 µm foils and 200 µm gap. 

The corresponding integrated spectra are shown in Fig. 4.30 for pions and electrons. One can see that 

the probability to exceed the threshold energy at 6-7 keV for electrons is approximately one order of 

magnitude higher than for pions due to generated and absorbed TR photons along the electron track. 

The difference in energy loss provides the basis for pion/electron separation. For the proposed TRT detector 

the cluster counting method is supposed to be used. Complementary methods such as likelihood 

and time-over-threshold [109] can also be considered. For optimization of TRT detector parameters, the 

simulations have been done for many different combinations of straw diameter and radiator parameters. 

Fig. 4.31 shows the dependence of pion efficiency on cluster energy threshold for 4 and 6 mm straws, 

2.6 cm radiator stack thickness, 15 µm foils and different gaps between foils. One can see that the rejection 

power (the inverse value of the pion efficiency) amounts to 1.4·10 3 for a single particle. The rejection 

power is practically independent on spacing gap of 150-250 µm between radiator foils and slightly worse 

for 400 µm gap. Our calculations show that radiators with thicker foils provide a slightly worse rejection 

capability compared to 15 µm. A better rejection performance can be achieved for thicker radiator stacks 

(i.e. larger number of foils), but at the cost of increasing detector size and additional material. The 

amount of material in the TRT detector should be kept as small as possible. The radiator configuration 

of 15 µm foils and 200-250 µm gaps seems to be in our case the preferable option. The thickness of one 

layer amounts to 2.5-3 cm. The optimal cluster energy threshold for 4 mm straws is around 6 keV. 

The expected rejection is better for 6 mm straws compared to 4 mm straws due to more efficient absorption 

of TR photons in the thicker straws. Due to the same reason the optimal cluster threshold for 6 mm 

straw is shifted to 8 keV compared to 6 keV for 4 mm straws. The pion efficiency does not depend on 

radiator parameters, but the electron efficiency does: the 90% efficiency threshold is shifted. The elec-


Probability to exceed energy 

1 

10 -1 

10 -2 

Pion and electron integral spectra 

0 2.5 5 7.5 10 12.5 15 17.5 20 

π 

electron 


Figure 4.30: Probability to exceed the threshold energy as a function of energy deposition in a single straw for 

pions and electrons. Different lines for elctrons correspond to straws in different layers: the lower line – to the first 

layer, the upper one – to the sixth layer. 

Pion efficiency at 0.9 of electron 

10 -2 

10 -3 

5 GeV, 4 mm straw, 2.6 cm radiator, 15 µm foil 

3 4 5 6 7 8 9 10 

Threshold (keV) 


10 -2 

10 -3 

5 GeV, 6 mm straw, 2.6 cm radiator, 15µm foil 

10 -4 

3 4 5 6 7 8 9 10 

High threshold (keV) 

Figure 4.31: Dependence of pion efficiency at 0.9 of electron efficiency as a function of cluster threshold for 

different spacing gap between radiator foils for 4 mm (left panel) and 6 mm (right panel) straws. 

tron threshold depends on the TR spectra which depend on radiator parameters (radiator foil thickness, 

gap size, total radiator thickness and detector thickness) in a very complex way. The choice of these 

parameters is subject to further optimization. 

The results above were obtained for 5 GeV primary particles. The dependence of pion efficiency on 

primary particle energy is shown in Fig. 4.32. The best rejection factor is expected at 2-5 GeV – the 

energy of most of secondaries in nucleus-nucleus interaction. The deterioration in rejection at very low 

energy is expected due to small TR yield, and at higher energy – due to relativistic rise of dE/dx energy 

loss in the straw gas for pions.



10 -3 

Dependence on particle energy 

1 10 

Primary particle energy (GeV) 

Figure 4.32: Dependence of pion efficiency at 0.9 of electron efficiency as a function of primary particle energy. 

4 mm straws, 2.6 cm radiator with 15 µm foils and 200 µm gap. 

Fig. 4.33 shows how the TRT rejection depends on detector efficiency or, in other words, on the number 

of operational straws on the particle track. The desirable rejection level can be achieved if straws are 

operational at least in 15-16 among 18 layers, i.e. the fraction of operational channels should be larger 

than 83-89%. 


10 -2 

Dependence on number of straws on track 

10 -3 

11 12 13 14 15 16 17 18 19 

Number of operational straws on track 

Figure 4.33: Dependence of pion efficiency at 0.9 of electron efficiency as a function of number of straws on the 

primary particle track. 20 GeV particle energy, 4 mm straws, 2.6 cm radiator with 15 µm foils and 200 µm gap. 

The main challenge for the TRT in the CBM both from the point of view of track reconstruction and 

of pion/electron separation is arising from multiparticle environment in real nucleus-nucleus interaction. 

Two pions crossing the same straw produce a twofold signal compared to a single pion and emulate a 

high energy cluster above the chosen threshold of 6-8 keV. Fig. 4.34 shows the rejection dependence


on the number of double hits on the particle track. The pion rejection decrease with increasing number 

of double hit straws. One possible solution to improve the particle identification ability in the presence 

of close particle tracks (double pions and conversion) is to use additional intermediate threshold around 

2-3 keV [110]. Such a technique is under way. Hit ambiguities can also be reduced by the arrangement 

of straw layers under stereo angles. 


1 

10 -1 

10 -2 

10 -3 

Rejection for double hits 

0 2 4 6 8 10 12 14 16 18 

Number of straws with double hit 

Figure 4.34: Dependence of pion efficiency at 0.9 of electron efficiency as a function of number of straws with 

double hit. 20 GeV particle energy, 4 mm straws, 2.6 cm radiator with 15 µm foils and 200 µm gap. 

4.3.2 Design of a TRT for CBM 

The proposed Transition Radiation Tracker has the following hierarchical structure: detector, module, 

submodule, chamber, straw layer, radiator layer. In order to achieve the necessary tracking performance 

it is currently anticipated to build three TRT modules. A module is a stack of three submodules. In the 

submodules the straws are arranged vertically and inclined under the stereo angles (0 ◦ ,+20 ◦ and -20 ◦ ). 

The submodules have the same active area as the dedicated module and they are segmented in the XYplane 

into several independent chambers. The segmentation depends on the distance to the beam axis 

and to the target. The size of each chamber is adapted to achieve a reasonable occupancy. The chambers 

are different in lateral size, but identical in the depth profile, which contains two sensitive straw planes 

and multi-layer radiator stacks in front of the tubes. The straws in the two layers of a chamber are shifted 

according to the each other perpendicular to the anode wire by the radius of the straw (see Fig. 4.28). The 

modules are ranged at distances of 4 m, 6 m and 8 m behind the target. The thickness of an assembled 

submodule amounts to 20 cm and of the full assembled TRT module 60 cm. The horizontal (X) and 

vertical (Y) sensitive sizes amount to (5.8×3.9) m 2 , (8.7×5.8) m 2 and (11.6×7.7) m 2 , for 1-st, 2-nd and 

3-th module. The total number of straws and readout channels are given in the Table 4.4. 

In the center of each module are physical holes. The sizes of the holes and the beam pipe have to be 

chosen accordingly to detect the charged particles with emission angles θ ≥50 mrad. The first module 

is located 4 m downstream from the target and should have maximal granularity. Each submodule of 

module-I consists of 20 chambers of five different types. The straws with 4 mm in diameter will be used 

for this chambers. The sensitive area of detecting elements vary from 4 cm 2 to ≈32 cm 2 . To reduce the 

occupancy, glass joints will be integrated to interrupt the galvanic contact of the anode wires in different 

chambers. Preliminary parameters of the straw chambers for the first module are given in Table 4.5 and


module straws detecting total sensitive 

∅=4 mm ∅=6 mm channels area, m 2 

I 45 696 — 82 944 6×23 

II 24 288 21 504 91 584 6×50 

III — 67 968 113 280 6×90 

TRT-detector ≈160 000 ≈300 000 ≈ 1000 

Table 4.4: Number of straws, channels and the common sensitive size of the TRT-modules. 

the arrangement of the chambers on the XY-plane is shown in Fig. 4.35. To accept particles emitted at 

θ ≥50 mrad the beam pipe diameter should not exceed 20 cm. Assuming an interaction rate of 10 MHz 

for minimum bias Au+Au collisions at 25 AGeV, the rate amounts to about 100 kHz cm −2 near the 

minimal opening angle, whereas the hit density for central events is about 0.045/cm 2 . 

Figure 4.35: The arrangement of five different chambers in a submodule of TRT module-I. The dots show the 

location of the glass joints. 

chamber number of chamber size straws per channels per 

type chambers X, cm Y, cm module module 

I-1 12 162 25 9600 19200 

I-2 12 162/143 20/10 9600 19200 

I-3 12 162 130 9600 19200 

I-4 12 143 130 8448 16896 

I-5 12 143 60 8448 8448 

Table 4.5: Parameters of the five types of chambers of module-I. 

The second module is located 6 m downstream from the target. Each submodule consists of 7 chambers 

of four different types. Straws with 4 mm and 6 mm in diameter will be used. Preliminary parameters 

of the straw chambers for module-II are given in Table 4.6 and the arrangement of the chambers on the 

XY-plane is shown in Fig. 4.36. For θ ≥50 mrad the beam pipe diameter should not exceed 40 cm. In


central Au+Au collisions at 25 AGeV the maximum hit density seen by the second TRT module at small 

forward angles is 0.022/cm 2 . 

Figure 4.36: The arrangement of the four types of straw chambers in a submodule of module-II. 

chamber number of straws chamber size straws per channels per 

type chambers ∅, mm X, cm Y, cm module module 

II-1a 12 4 165 95 8880 17760 

II-1b 12 4 165/150 45/22 8880 17760 

II-2 12 6 165 150 6528 13056 

II-3 48 6 136 150 21504 43008 

Table 4.6: Parameters of the four straw chamber types of module-II. 

The third module is located 8 m downstream from the target. Each submodule consists of 15 chambers of 

five different types. Only straws with 6 mm in diameter will be used. The preliminary parameters of the 

straw chambers for module-III are given in Table 4.7 and the allocation of the chambers on the XY-plane 

is shown in Fig. 4.37. In module-III the chambers 1,2 and 3 are arranged symmetric to the vertical axis 

normal to the beam line. The beam pipe diameter should not exceed 60 cm. In central Au+Au collisions 

at 25 AGeV the maximum hit density seen by the third TRT module at small forward angles is 0.014/cm 2 . 

Figure 4.37: The arrangement of the five chamber types in submodule of module-III.


4.3.3 Detector elements 

4.3.3.1 Straws 

chamber number of chamber size straws per channels per 

type chambers X, cm Y, cm module module 

III-1 6 58 117 1152 2304 

III-2 6 58 150 1152 2304 

III-3 6 58 75 1152 1152 

III-4 96 136 150 43008 86016 

III-5 48 136 75 21504 21504 

Table 4.7: Parameters of the five types of chambers of TRT module-III. 

The straw tubes are made of two layers of polyimide film. The Kapton film 160XC with graphite loading 

is used for the inner layer and the Kapton film 100VN is used for the outer skin of the straw tube. 

The inner surface of the 100VN film is aluminized with a thin layer of about 0.2 µm thickness. The 

summarized specifications is given in Table 4.8. 

description specifications 

Kapton film polyimide type 100VN polyimide type 160XC / 275XC /other 

density 1.42 g/cm 3 1.41 g/cm 3 

aluminium layer (0.20±0.08) µm — 

film layer (25±2) µm (25±2) µm 

film resistivity


4.3.3.2 Straw internal elements 

The gold-plated tungsten wire type 861 (Luma) – 30 µm in diameter – will be used for the anodes. The 

tension of the wire is 0.7 N. Polycarbonate end-plugs made by the method of pressure moulding are used 

to fix the wires at the straw ends. These elements are inserted into the straw against their stop. This excludes 

their displacement later on. There are grooves on the outside surface of the end-plugs to connect 

the internal straw volume to that common to all the straws; this gas manifold is used for their blow-down. 

There is also a gutter to install a contact spring which allows one to connect the cathode to the common 

ground of the chamber. The wire goes through Cu pins inserted in the end-plugs. The inner diameter of 

the tube is 100 µm and the outer one is 700 µm. The top pin is then crimped, the wire is tensioned and the 

bottom pin is finally crimped. To increase the straw rate capability, the anode of a straw can be divided 

into two independent detecting elements. Glass capillary tubes with parameters given in Table 4.10 will 

be used for the glass-joints as shown in Fig. 4.38. 

parameter specification 

outer diameter 0.25 mm 

inner diameter 0.1 mm 

weight 0.19 mg/pc 

length 4, 5 or 6 mm 

Table 4.10: The main parameters of the glass-joints. 

Figure 4.38: Schematic view of a glass-joint. The glass-joint holds and insulate two ends of an interrupted anode 

wire. 

To compensate the sagitta of the wires, one spacer per 80 cm length of the anode wire will be used. 

These spacers with a central hole of 100 µm in diameter also made of polycarbonate by the method of 

pressure moulding. The dimensions and mass of the spacers are minimized, e.g. a prototype for straws 

of ∅=6.00 mm has a diameter of 5.97(+0 -0.018) mm and the mass of one spacer amounts to 15 mg. The 

spacers are pasted on the anode wires with epoxy glue before mounting the wires in the straws. 

4.3.3.3 Radiators 

Each radiator layer is a stack of 140 polypropylene foils of 15 µm thickness. The foils are regularly 

spaced with gaps of 200 µm. The support mesh is placed between neighbouring films. The size of the 

mesh cells is about 7×7 mm 2 . Total radiator layer thickness is ≈3 cm. The radiator layer is suspended 

on springs in front of the straw layer and fixed to the inner wall of the frame. The radiation length of a 

radiator layer (140 foils×15 µm) amounts to X/X0=0.443%. 

4.3.3.4 Support frame and subframes 

To provide an external protection and to make a monolithic double layer submodule, the chambers are 

mounted into a carbon structure support. A subframe is a part of a chamber. It contains two – orthogonal 

to the straws – carbon fiber bars and two – parallel to the straws – thin aluminium bars. The temperature 

changes leads either to the appearance of an effort, stretching the straws additionally, or to their curvature.


Aluminium bars minimize this effect because the expansion coefficients for Al and Kapton are quite close 

to each other (23.2·10 6 K −1 and 18·10 6 K −1 , respectively). Carbon bars have no temperature expansion 

and provide a high long time stability of the chamber position in the frame structure. The accuracy of 

the chamber installation in the frame structure will be 50 µm. Each subframe contains gas manifolds and 

provides the fixation of the radiators, electronic boards etc. 

4.3.3.5 Material budget 

The TRT detector should consists of low-Z materials. Lightweight construction will be used for the 

support frames. The use of metallic components should be minimized. The main materials and elements 

of the detector are listed below in Table 4.11: 

element material size rad. length, % 

wire W(Au) ∅30µm 0.007 

joint glass ∅0.25 mm×5 mm 0.115 

straw tube Kapton 68µm 0.005 

glue — 0.003 

spacer — ∅6 mmx6 mm; 15 mg 0.1 

gas Xe(75%)/CO2(25%) ∅6 mm 0.046 

∅4 mm .032 

end-plug lexan ∅6 mm×10 mm; 250 mg 1.003 

∅4 mm×10 mm; 150 mg 0.903 

radiator polypropylen 140x15µm 0.875 

crimping tube Cu ∅.7 mm×7 mm 

Table 4.11: Size and radiation length of some elements of the straw chambers. 

The radiation length of the whole detector amounts to X/X0≤15% if we assume one module with 4 mm 

straws and two modules with 6 mm straws. The frame contains additionally motherboards for ∅=4 mm 

straws – 0.52 cm 3 /channel and for ∅=6 mm straws – 0.78 cm 3 /channel. Table 4.12 shows the common 

length of the horizontal and vertical bars in the XY–plane. The construction is more complex in the 

horizontal direction as in the vertical one. The choice of bar material for the Y direction will be done after 

the design of the frame. The temperature coefficient, the radiation length and cost for both construction 

materials – Al and the carbon fiber – will be taken in account. Table 4.12 shows the amount of carbon 

fibers needed for the all TRT frames. Preliminary calculation of the carbon fiber amounts to 0.6 kg/m in 

X and 0.3 kg/m in Y-direction. 

module X Y 

length, m mass, kg length, m mass, kg 

I 384 65.9 197 39 

II 514 86.5 416 82.3 

III 880 151.2 864 171.1 

total 1778 303.6 1477 292.4 

Table 4.12: Amount of carbon fiber for the straw chamber frames in all three modules.


4.3.4 Gas supply system 

The gas system is designed to provide the operation of all three TRT modules consisting of ≈160 000 

straws with total volume of ≈ 4.7 m 3 . The gas volumes in the different modules are shown in Table 4.13: 

module straw length, m gas volume 

∅=4 mm ∅=6 mm m 3 

I 37 422 — 0.5 

II 14 208 44 651 1.5 

III — 91 377 2.7 

total 51 630 136 028 ≈4.7 

Table 4.13: The gas volume of all straws. 

The operating conditions expected at the CBM experiment, and the specific characteristics of transition 

radiation detectors for acceptable electron to pion separation of ≈10 −2 require the following gas 

properties: 

• The gas must be a Xe based mixture providing efficient X-ray absorption. 

• The gas must be as fast as possible to minimize pile-up effect. 

• The gas must guarantee stable operation of the straws in a sufficient high-voltage range and for 

very high fluxes of particles crossing the straws. 

Taking into account the experience of ATLAS TRT [111] which showed an essential ageing of straws in 

gas mixture (Xe/CO2/CF4) we propose to use gas mixture Xe(75%) + CO2(25%), which shows stable 

operation of the straws at high counting rates up to 18 MHz. The variation of percentage rates of gas 

components within 1% is acceptable. Each chamber contains two straw layers and consequently two 

independend gas units. The total number of gas units for TRT detector amounts to 612 with a volume 

of 4.7 m 3 . The gas leakage level is less than 1 mbar/min/bar. Assuming an overpressure of 20 mbar the 

gas leak for the whole TRT detector amounts to ≈0.2 m 3 /day. The minimal gas flux (similar required at 

LHC) is 0.058 m 3 /min or 83 m 3 /day for all three modules. The gas flow through module-I is 12.2 m 3 /day. 

In all cases the large volume of expensive Xe gas is required for long time of the operation. Therefore the 

proposed gas system should provide recirculation of gas mixture and its purity. The primary purpose of 

the CBM TRD gas system (Fig. 4.39) is to provide the gas mixture to the detector segments at the correct 

pressure. Refer to Table 4.14 for a list of gas system parameters. The system operates nominally as a 

closed circuit gas system with the majority of mixture recirculating through the three modules connected 

in parallel [112]. During normal operation a small amount of fresh mixture is added and an equivalent 

quantity of the existing mixture is vented to the buffer. The gas system can be operated in an open 

configuration for purging. The mixture circulation rate through the compressor is 150 l/min at 250 mbar. 

The gas system contains two compressors (Com1, Com2), one active and one spare. The mixture from 

the compressor goes to the supply line through the check valves (CV4 or CV5). The 250 mbar output 

pressure from the compressor is reduced to 50 mbar by the pressure regulator (PCV1) and supported with 

the back pressure control valve (BPCV1). Then 50 mbar pressure is reduced to 20 mbar supply pressure 

by the pressure regulator (PCV2). The return gas manifold is maintained at 2 mbar above atmospheric 

pressure by a differential pressure transmitter (PT1) and pneumatic PID controller (PIDC) that operates 

a bypass valve (BV1). The bypass shunts flow from the compressor discharge line directly back to 

the compressor’s inlet to support the return manifold pressure stable. A second bypass valve (MV3) is 

manually adjusted to enable the automatic control loop to be used within its optimum range.


Figure 4.39: General layout of circulation gas loop. 

The flow indicating transmitter FIT2 will measure the recirculating flow. The measurements of the fresh 

mixture (FM1, FM2 and FM3) and flow through the flow indicating transmitter (FIT1) give a possibility 

to estimate the detector leakage. The purity and composition of recirculating mixture is monitored using 

oxygen, carbon dioxide and humidity analyzers. A fraction of the recirculating mixture can be passed 

through a dryer to remove the moisture. Mixture coming out through the BPCV1 is collected with the 

buffer and then compressed by the high pressure compressor (HPC) to one of the cylinders (C1 or C2). 

The compressed mixture can be used as the part of make up mixture or for recovery of Xenon gas. A 

computer driven data acquisition and control systems monitor all of the process variables including the 

barometric pressure. The computer system flags quantities which fall outside of predefined limits and 

initiates corrective action. However, where the safety of equipment or personnel are affected, a relay 

based system connected to redundant set of sensors control the pressure levels of all key based controls 

fail. The vent lines, associated valves and bubbler are sized to allow rapid venting of the TRD segments 

mixture to prevent a high internal pressure in the case of the fast barometric pressure fall. 

mixture Xe(75%) + CO2(25%) 

compressors output pressure 250 mbar 

mixture supply pressure 20 mbar 

mixture return pressure 2 ± 0.05 mbar 

recirculation flow 80 - 150 l/min 

make-up mixture flow 0.2 - 5 l/min 

water content < 80 ppm 

gas leakage < 0.2 m 3 /day 

Table 4.14: Performance of the gas system.


4.3.5 Detector readout 

Two types of the track position readout were considered: the anode readout (based on the drift time 

registration) and the cathode readout (based on the amplitudes of the pulses induced in the pads). Both 

of them have certain advantages and shortcomings. We have recognized the anode readout as more 

advisable due to possibility of application of faster electronics (important for the high-rate properties of 

the system) and lower distortion of the position resolution due to angular effects. 

4.3.5.1 Behavior of the detectors under high-rate conditions 

For minimum bias Au+Au collisions particle flux ranges from 0.25 kHz cm −2 (at the edge of module-III) 

to 100 kHz cm −2 (at the central part of module-I). For central Au+Au collisions, the hit density amounts 

to 0.045 cm −2 for the inner part of the first TRT module. 

The drift cell size is defined by the intersection of straws with different orientations (0 ◦ ,+20 ◦ and -20 ◦ ). 

The cross section area defined by three straw tubes with 6 mm in diameter amounts to 0.4 cm 2 . Taking 

into account the shift of parallel straws by a half straw diameter and 4 mm tubes, than the size of a 

drift cell will be reduced by one order of magnitude and amounts to ≈0.04 cm 2 ). Let us assume that 

the straws are 6 mm thick and the detector is filled with the Xe/CO2 mixture. Maximum anode current 

pulse duration (cluster charge collecting time) may be estimated as 70-80 ns. Then the duration of the 

pulse from the straw tube anode (in the case of aggressive base line restoration) is about 100 ns (as in the 

ATLAS TRT). For central collisions the occupancy will increase to 4%. The stereo angle arrangement 

of the tubes gives the possibility to disentangle multihits in straws. According to table 4.5 the shortest 

anode wires will have a length of 10 cm, corresponding to a single straw occupancy of 18% for the most 

forward angles. The performance of the TRT at high rates and for high multiplicities, in particular the 

pion suppression capability, is subject of further investigations. 

4.3.5.2 Spatial resolution 

The use of fast front-end electronics should provide the necessary spatial resolution. There is no doubt 

that the assumed requirement of the 200 µm spatial resolution may be fulfilled by the straw drift tubes. 

An anode signal provides the start of the time measurement. The stop is deduced from an external detector, 

e.g. the delayed signal of a scintillator telescope or a RPC. The resulting arrival time of the drift 

electrons, after offset subtraction and calibration is converted into a distance from the sense wire. The 

drift-time measurement was performed with a TDC with bin width around 1 ns. Assuming a drift velocity 

smaller than 60µm/ns in the straws the coordinates can be obtained with an accuracy of better than 

200µm. Fig. 4.40 shows the results of the observed spatial resolution for a straw of 50 cm length and 

4 mm in diameter for a low rate (top) and for a high rate up to 18 MHz (bottom) [104] [113]. The particle 

track has been measured with an accuracy of 5µm using a Si-microstrip telescope as a master detector. 

4.3.5.3 Front end electronics 

In the straw drift chambers the signal is being received from the detector edges, not from the whole surface. 

Therefore there is no problem with the signal transmission from the anode to the amplifier input. 

According to our experience from the design of the large area straw tracker for the COMPASS experiment, 

it is hard to achieve the satisfactorily low electromagnetic interference level when the analog and 

digital part of the electronics are placed on a common board. Therefore we would suggest to divide


500 

400 

300 

200 

100 

0 

250 

200 

150 

100 

50 

0 

0 MHz 

σ = 128 μm 

ε = 86 % 

-1 -0.5 0 0.5 1 

Residual (mm) 

18 MHz 

σ = 183 μm 

ε = 54 % 

-1 -0.5 0 0.5 1 

Residual (mm) 

Figure 4.40: Spatial resolution obtained with straws by low (top) and high (bottom) particle flux [104] [113]. 

the front end electronics into two separated parts: the first one would contain the Amplifier-Shaper- 

Discriminator circuits and it would be placed close to the straw tube endplugs and second one, contains 

TDCs, would be mounted on the frame. The signal transmission between the analog and digital part 

could be realized using twisted pair cables or optical fibers, depending on the ground connections, power 

consumption, time jitter etc. This needs more thorough analysis. The analog part may be realized using 

the ASD-BLR integrated circuits, designed for the ATLAS TRT. However, taking into account that 

the ASD circuit was designed several years ago, we consider a design of the new integrated circuits as 

advisable. We are ready to undertake this task and design the chip having better parameters and based 

on modern technologies (e.g. AMS 0.35 µm SiGe HBT). The design would be based on the project of 

the Amplifier-Shaper-Discriminator chip, which was prepared by the team from the Institute of Radioelectronics 

and specialists from the Institute of Microelectronics and Optoelectronics from the Warsaw 

University of Technology. The new chip was foreseen mainly as an up-to-date, improved replacement 

of the ASD family chips in the updated COMPASS experiment and other future high energy experiments. 

The project was suspended because of the lack of the financial support. However, the functional 

and electrical design of the chip was done, as well as simulations of the chip layout based on the AMS 

0.8 µm Bi-CMOS technology, which, unfortunately, becomes to be obsolete nowadays. In order to apply 

this design for the CBM TRT slight changes of the electrical scheme would be necessary, as well as a 

migration to newer technology, like AMS 0.35 µm SiGe HBT. For the digital part it would be necessary 

to design chips containing TDC and interface to the link, which connects the FEE with the higher levels 

of the data acquisition system.


4.3.6 Costs 

Table 4.15 contain information about the costs for straw based TRT modules, gas supply and front-end 

electronics. The costs are preliminary and will be worked out in more detail during the next steps of 

research and development. 

4.4 Detector ageing 

item costs (k) 

straw tube detectors 2000 

radiators 300 

support frames 1000 

gas system 300 

services(HV, LV, cooling) 2000 

FEE 3000 

total 8600 

Table 4.15: Preliminary cost of the main detector components. 

To estimate the dose per year of operation for the TRD detectors we use the multiplicities of charged 

particles calculated with the UrQMD model [114, 115] for Au+Au events at 25 AGeV beam energy. We 

assume a reaction rate of 10 7 /s and an equivalent beam on target period of two months per year (5×10 6 

seconds). 

The rates, fluxes and doses for different polar emission angles are given in Table 4.16 for the three TRD 

stations. In the regions closer to the beam the integrated particle flux amounts to 5 × 10 11 charged 

particles per cm 2 per year of operation. For 6 mm thick detectors filled with a Xe-CO2 gas mixture 

and operating at a gas gain of 10 4 , this translates into 26 or 53 mC per cm of wire per year for a 2 or 

4 mm anode pitch, respectively. This integrated charge is considerable, so that ageing phenomena need 

to be considered. While the gas mixture itself is non-polymerising, the materials in contact with the 

gas may outgas pollutants that can produce severe ageing. Therefore, a careful selection of the chamber 

assembly insulators and epoxies, as well as of the components of the gas system, must be carried out. 

Specific ageing tests will therefore be part of the detector development program. The expected current 

in the most loaded regions of the TRD is 26 nA/cm 2 . Several years of CBM could be accumulated in 

days or weeks in accelerated ageing tests at current densities several times higher that this. However, the 

behaviour of the ageing processes as a function of the current density should be taken into account, since 

the rate of ageing is often lower the more accelerated the test is. On the other hand, many materials have 

already been tested and validated at other experiments (LHC, HERA) [116] for similar or higher lifetime 

requirements, thus making the task easier. 

4.5 Infrastructure 

The common infrastructure required for the TRD system comprises: electrical power (total estimate 

power consumption can be up to 40 kW), cooling water, crane for mounting of individual detectors. 

Note that, the gas system, being a dedicated part of the TRD system, is factorized into detector costs.

4.5. Infrastructure 119 

Polar emission angle Rate Flux Dose 

[mrad] [kHz/cm 2 ] [yr −1 cm −2 ] [krad/yr] 

station 1 

50 - 100 100 5.0×10 11 16.0 

100 - 150 53 2.7×10 11 8.5 

150 - 200 26 1.3×10 11 4.2 

200 - 250 17 8.5×10 10 2.7 

250 - 300 9.6 4.8×10 10 1.5 

300 - 350 7.1 3.6×10 10 1.1 

350 - 400 4.4 2.2×10 10 0.7 

400 - 450 2.0 1.0×10 10 0.3 

450 - 500 0.9 4.5×10 9 0.14 

station 2 

50 - 100 50 2.5×10 11 8.0 

100 - 150 25 1.3×10 11 4.0 

150 - 200 13 6.5×10 10 2.1 

200 - 250 7.5 3.8×10 10 1.2 

250 - 300 5 2.5×10 10 0.8 

300 - 350 3.3 1.7×10 10 0.5 

350 - 400 2.1 1.1×10 10 0.3 

400 - 450 1.0 5.0×10 9 0.16 

450 - 500 0.4 2.0×10 9 0.06 

station 3 

50 - 100 32 1.6×10 11 5.1 

100 - 150 15 7.5×10 10 2.4 

150 - 200 7.9 4.0×10 10 1.3 

200 - 250 4.8 2.4×10 10 0.8 

250 - 300 2.7 1.4×10 10 0.4 

300 - 350 2.0 1.0×10 10 0.3 

350 - 400 1.3 6.5×10 9 0.2 

400 - 450 0.6 3.0×10 9 0.1 

450 - 500 0.3 1.5×10 9 0.05 

Table 4.16: Rate, flux and dose in the three TRD stations for different polar emission angles.


4.6 Working packages and timelines 

Activity Period 

performance simulations of detector variants 2005 

global tracking 2005-2006 

S/B optimization of detector layout 2005-2006 

detector prototypes development/tests 2005-2006 

design and tests of preamplifier/shaper prototypes 2005-2006 

matching data/simulations 2005-2006 

performance simulations of high-rate and high-occupancy 2006 


design of preamplifier/shaper 2007-2008 

design of digital readout 2008-2009 

data preprocessing/trigger 2008-2009 

detector prototypes and FEE tests 2007-2009 

final detector layout and technology choice 2008-2009 

mechanical design 2008-2009 

technical design report 2010 

production, installation, tests 2011-2012 

Table 4.17: Outline of the future TRD activities and timelines.

5 Resistive Plate Chambers (RPC) 


The main task of the TOF subdetector system is the identification of hadrons. The anticipated performance 

and acceptance is presented in chapter 14. It is obvious that the performance of the CBM 

experiments improves with an improved time resolution of the TOF subsystem. A better resolution does 

not only improve the particle - identification capability of the detector system, it also would allow for 

shorter distances, over which the time-of-flight is measured and thus for a more compact design. Provided 

that the counters are able to sustain the anticipated high rates as presented in section 12.1 a better 

time resolution would also allow for a substantial reduction of cost. 

One of the key tasks of the TOF system is the separation of pions and kaons. In order to achieve a 

two to three sigma separation with time resolutions in the order of 80 ps, flight pathes of the order of 

10 m are necessary. Therefore the full coverage of the same solid angle as the STS system requires 

an area of the TOF system in the order of 150 m 2 . Such an area cannot be equipped with traditional 

scintillator/ photomultiplier counters at an affordable cost. The only choice and the one adopted by the 

CBM collaboration is to develop a high resolution and high rate version of multigap timing RPCs. 

RPCs (Resistive Plate Chambers) is a 23 years old technology [117], but only 4 years ago it was shown 

that it is possible to use RPCs for precise time of flight measurements at normal conditions of pressure 

and temperature with inexpensive materials [118]. There are already several experiments foreseeing 

or working with timing RPCs like ALICE [119, 120], FOPI [121], HADES [122], HARP [123] or 

STAR [124]. 

A system of similar size as needed for the CBM detector is currently being installed in the ALICE 

experiment [119]. The requirements imposed by the running conditions of CBM are, however, very 

different from the situation within ALICE at LHC. For CBM a system time resolution of at least 80 ps 

is necessary, which has to be obtained over the full acceptance and thus under different count rate loads 

ranging from 20 kHz near the beam axis to about 1 kHz at 28 o . Due to the fixed target geometry of the 

CBM experiment also the hit density changes significantly from 10 −2 cm −2 (2.5 o ) to 6 · 10 −4 cm −2 (27 o ). 

Based on those considerations the directions for R&D for the CBM TOF wall are: 

• Explore the rate capability of the RPC detector concept. 

Towards this goal materials different from float glass with a bulk resistivity of Ω=10 11 Ωcm have 

to be investigated. Also environmental parameters like the temperature could help to improve 

the rate capability and have to be studied. New materials and operation conditions have to be 

complemented by aging studies. 

• Explore the time resolution limit of various RPC readout concepts for large area coverage. 

The uniformity of the response over the full detector surface is a key element for the physics 

performance. Due to the large differences in hit density and rate various readout scenarios at 

different polar angles could present the optimum solution. Therefore the limitations of a pad or a 

strip based readout need to be evaluated. 

• Develop fast front-end electronics and digitisation system. 

Due to the fast rise time of the detector signals, signal propagation over large distances and pro- 

121


cessing in highly integrated electronics represents a serious problem. Learning from existing solutions 

[119, 121], the optimal strategy needs to be developped. 

• Perform detailed simulations for RPCs embedded into the CBM system. 

Besides time resolution the requirements posed onto the RPC system depend to some extend on the 

way the TOF subsystem is integrated into the full experiment. The TOF - Hits need to be matched 

with the tracks found in the TRD stations. There resolution and geometry defines the necessary 

position resolution / granularity that the TOF detector has to deliver. The TOF system layout needs 

to be optimized with respect to those needs. 

These R&D items are currently actively pursued. Since the development of timing RPCs started only in 

the year 2000 [118] its final potential is not known today. Therefore a design proposal for the CBM TOF 

wall does not exist yet. However, the assumptions made in chapter 14 of a time resolution in the order 

of 80 ps seem to be reachable. Further improvements are not excluded and would result in a significant 

improvement of the PID capability of the experiment. At this moment of time, it is not justified to base 

the overall layout of the concept on such possible improvements. Therefore in the following a baseline 

of an overall uniform time resolution of σt=80 ps is used. 

In the following the progress and the status in the various research areas are described. 

5.2 Simulations 

5.2.1 Input data and parameters of simulation 

The generic CBM simulation is described in chapter 14. The simulation presented here deals with the 

specific aspects of the TOF wall and also uses common CBM software framework CbmRoot, which is 

based on the ROOT and Geant3 packages. The TOF detector a 10 × 10 m 2 plane with a 0.8 × 0.8 m 2 

hole in the middle is placed 10 m away from the target. It contains the following material layers: Al-Gas- 

Glass-Gas-Al with corresponding widths of 0.2 − 0.12 − 0.54 − 0.12 − 0.2 cm. The design and material 

budget were assumed similar to those of the ALICE-TOF detector. The other CBM sub-detectors were 

taken by default from the CbmRoot package. 100 UrQMD Au+Au central events at E/A=25 GeV were 

used as input particles. Momentum and angular distributions of primary pions, kaons and protons are 

shown in chapter 14. 

Occupancy appears to be one of the most important parameters influencing the TOF detector performance. 

The density of charged tracks in the TOF plane is shown in Fig. 12.4. It is found that close 

to the beam pipe the maximum occupancy is about 0.6 primary tracks/dm 2 , and about 0.5 secondary 

tracks/dm 2 . For 10 cm 2 TOF cells, the maximum occupancy is about 11%. The contribution of secondaries 

therefore is significant. 

In total 1080 tracks per event cross the TOF plane of which 440 (40%) are primaries and 650 (60%) are 

secondaries. The composition of the secondaries is: 54% e ± , 19% p, 14% π ± and 13% µ ± . The origins 

of secondaries are: 6% ECAL, 12% TOF, 18% TRD3, 13% TRD2, 10% TRD1, 4% RICH and 16% 

STS. 

5.2.2 Matching of tracks with TOF 

Track matching is an important task for the TOF detector. Let us consider a single track in the last TRD 

station positioned 8 m away from the vertex. Extrapolating this track to the TOF plane, we define ΔR to 

be the deviation of the track from the corresponding TOF hit and Rmin to be the minimum distance from

5.2. Simulations 123 

the given hit to the others. ΔR is determined by the multiple scattering between the last TRD station and 

the TOF plane (see Fig. 5.1 for its distribution). The average deviation is ∼ 0.4 cm but there is a long 

tail to larger values. Rmin depends on the hit density in the TOF plane and its distribution is shown on the 

right side panel of Fig. 5.1. 

number of tracks per event (1/bin) 

90 

80 

70 

60 

50 

40 

30 

20 

10 

deviation of the track from the hit 

hPrimTOFdeltaRho 

Entries 41536 

Mean 0.4311 

RMS 0.492 

0 

0 0.5 1 1.5 2 2.5 3 3.5 4 

ΔR 

(cm) 

number of tracks per event (1/bin) 

25 

20 

15 

10 

5 

distance to the closest hit 

hPrimTOFminRho 

Entries 44457 

Mean 12.52 

RMS 8.689 

0 5 10 15 20 25 30 35 40 

Rmin 

(cm) 

Figure 5.1: ΔR distribution (left) and Rmin distribution (right) obtained for central UrQMD collisions of Au + Au 

at Ebeam=25 AGeV. 

Clearly there is a correlation of the minimal distance to the closest hit with the distance to the beam pipe 

ρ (Fig. 5.2). The granularity of the detector setup can be optimized with respect to the anticipated hit 

density. 

(cm) 

Rmin 

40 

35 

30 

25 

20 

15 

10 

5 

0 

0 50 100 150 200 250 300 350 400 

ρ (cm) 

Figure 5.2: RPC Hit distribution as function of the minimal distance to the closest next hit Rmin and the distance 

to the beam pipe ρ. 

For ΔR > Rmin it is difficult to find a track corresponding to the particular hit, and such a track may be 

considered as mismatched. This gives us an estimate of the mismatching fraction. Comparing ΔR and 

Rmin distribution (Fig. 5.1) we can find only a small number of tracks with ΔR > Rmin. The simulation 

confirms this conclusion: the fraction of the mismatched tracks is about 1.53%. These results may be 

nevertheless improved by moving the TRD station closer to the TOF plane. Such improvement of track 

position accuracy could also benefit the overall TOF performance. Our plan is to continue the simulation 

with the real TOF structure to find an optimal design and to develop matching and particle identification 

algorithms using the experience gained by the ALICE-TOF group [120, 125]. 

1.6 

1.4 

1.2 

1 

0.8 

0.6 

0.4 

0.2 

0


5.3 RPC properties 

5.3.1 Rate capability 

Currently the detection of MIPs over large areas with RPCs can be made at rates close to 3 kHz/cm 2 [126, 

127], while at medium gas gains, around 10 5 , and small active areas, counting rates over 10 7 Hz/cm 2 [128, 

129] have been demonstrated. For timing RPCs [118] the current maximum counting rate capability is 

close to 2 kHz/cm 2 [130]. However, these counters operate at rather extreme conditions (enormous gas 

gain over 10 12 and avalanche development in a deeply space-charge saturated mode [131]), which may 

render difficult the operation at high counting rates due to instabilities arising from the cathodes [128]. 

In practice, the counting rate capability of RPCs is also strongly conditioned by the availability of suitable 

electrode materials with medium resistivity and good mechanical characteristics. Bulk resistive 

materials used so far include bakelite and glass (the most common RPC materials), doped ABS plastic 

[132], silicon or gallium arsenide [129] and other ad hoc materials [133]. Layers of germanium [134] 

or SiC [135] deposited on insulating substrates have been also considered. Difficulties may also arise 

from the possible appearance of permanent discharges at medium electrode resistivities (roughly 10 4 to 

10 8 Ω·cm [129]), while at lower resistivity values the sparks progressively become more powerful. 

As the extension of the current counting rate capabilities of RPCs up to 50kHz/cm 2 will be an important 

part of the requirements for the inner part of the CBM TOF Wall, we tried to identify suitable electrode 

materials. Our efforts, so far, concerned the semiconducting glasses and the doped polymers, being the 

results developed below. We considered also an alternative approach that, although it will probably not 

meet the rate requirements over the full TOF area, may help to extend the area covered by RPCs made 

of ordinary flat glass. This approach involves a moderate warming of the detectors, exploiting the very 

strong dependence of the glass resistivity with temperature. 

5.3.1.1 Rate capability improvement using polymeric materials 

In this section we describe the main results obtained with the so called antistatic plastics, which are 

available with catalogue resistivities ranging from 10 8 to 10 12 Ωcm. 

The detectors, with an active area of 9 cm 2 , were made by a stainless steel cathode and an ENSITAL R○ 

SD [136] plastic anode, defining a single 0.3 mm wide gas gap (Fig. 5.3). More details are available 

in [137]. 

Currents and counting rate 

Figure 5.3: Schematic view of the detector structure. 

The chamber current and counting rate per unit area as a function of the applied voltage is shown in 

Fig. 5.4.

5.3. RPC properties 125 

For currents below 70 nA/cm 2 , corresponding to a saturation counting rate of 27 kHz/cm 2 , the chamber 

follows a linear current growth law. Although gas counters should in principle follow an exponential law, 

it is well known that RPCs follow a linear law at high gains due to the space-charge effect. 

Using the method suggested in [138] it was estimated that during the measurements shown in Fig. 5.5 

the resistive electrode presented a resistivity around 20 GΩcm. 

Figure 5.4: Current and counting rate per unit area as a function of the applied voltage. The chamber deviates 

from the linear current growth close to 70 nA/cm 2 , corresponding to a saturation counting rate of 27 kHz/cm 2 . 

Time resolution 

As shown in Fig. 5.5, the time resolution remained essentially unchanged, at a level around 90 ps σ, from 

2 kHz/cm 2 to 27 kHz/cm 2 . 

It should be noted that previous experience with timing RPCs in particle beams and with annihilation 

photons has shown that while single-gap counters may reach a time resolution of 60 ps σ in particle 

beams [139], similar counters irradiated with 511 keV photon pairs reach only about 90 ps σ. This 

fact may be attributed to the larger statistical variance of the primary currents arising from the latter 

irradiation method (see [140]). 

Brief characterization of the resistive material 

External tests to the resistive material were performed for obtaining some information about its longterm 

dynamic behavior. A second conductive electrode made with silverloaded epoxy was formed over 

the ENSITAL plate and the sample placed in a temperature-controlled enclosure (the resistivity sharply 

decreases with temperature) under a steady flow of tetrafluorethane. A fixed voltage was applied across 

the sample and the current continuously monitored. After some time, the voltage was set to zero, except 

for a few brief moments to allow a fast resistivity measurement. 

The results can be seen in Fig. 5.6. Initially the resistivity rises exponentially with the transferred charge, 

but later seems to progressively approach a stable value. This value depends on the applied voltage but, 

remarkably, the final currents are similar for different applied voltages. When the voltage is removed the 

resistivity recovers slowly in time, eventually retrieving its initial value.


Figure 5.5: Time resolution as a function of the counting rate, essentially unchanged from 2 kHz/cm 2 to 27 

kHz/cm 2 at a level around 90 ps σ. The vertical spread of the points is mainly due to statistical fluctuations and the 

horizontal spread due to the different working voltages at which the points have been taken. 

Although such measurements may not rigorously represent the behavior of the material, when used as 

an electrode (namely given the different current injection method), it seems that a qualitative agreement 

can be perceived. For instance, at the highest counting rates the chamber current was up to 15% higher 

at the beginning of each run and then decayed rapidly to its equilibrium value. The runs lasted typically 

for 20 minutes, followed by a low current period of about 10 minutes. For such intermittent use a 

kind of equilibrium may be reached between resistivity increase due to current flow and its recovery 

by resting, allowing a reasonable agreement between the indirect resistivity measurements mentioned 

before (dashed line in Fig. 5.6) and the external ones. 

Figure 5.6: Externally measured resistivity of the ENSITAL plastic as a function of the transferred charge. Tests 

were done with applied voltages of 100V and 600V and the electrodes allowed to rest for 170 hours between 

measurements. The horizontal dashed line marks the indirect measurement mentioned in section 5.3.1.1. 

Conclusions 

The present result establishes the basic feasibility of timing measurements with RPCs at rates of tens of 

kHz/cm 2 while keeping a time resolution below 100 ps σ. This represents an improvement of over one 

order of magnitude in counting rate as compared to the highest rates reported so far. For this particular 

study we used a commercially available plastic material, showing that small area timing RPCs can be 

made with plastic anode electrodes. A severe, but reversible, increase of the plastic resistivity with the


transferred charge was observed. While this characteristic may not be of much importance if the counters 

are to be used in a low duty cycle situation, the material may not be suitable for application in CBM. 

5.3.1.2 Rate capability improvement using semiconductor glasses 

Semiconductive glass is one of the best candidate for RPCs electrode material with its low electron 

conductivity and its relatively small resistivity of 10 8 - 10 11 Ωcm. One of the main disadvantages of 

semiconductive glasses is the absence of mass production, and, as a consequence, high prices of the 

prototype samples. Semiconductive glasses have been developed in Russia since the 60’s of last century. 

There are 3 known types of semiconductive glasses depending on the base material selected for their 

production: phosphate, silicate and borosilicate glasses. These glasses are characterized by electron 

conductivity and contain the oxides of transition elements. They have black color and are opaque for 

the visible light. The technology of semiconductive glass fabrication is more complicated than for usual 

window glass, and before starting a semi-industrial production of the glass a few problems should be 

solved: 

• the best chemical composition should be found to get glasses with electron conductivity and the 

required resistivity; 

• because the color of the glasses influence the heat transfer properties, the development of special 

production technology and equipment different from that of usual window glass is needed; 

• a method for removing the internal glass stresses, which arise during its cooling, has to be devised. 

The control of the glass fritting is not possible by the technique routinely adopted for window 

glass, in particular, by polarimeters, due to the opacity of the proposed glasses. Another method 

to control the degree of fritting should be developed; 

• the problem of glass crystallization should also be solved to get an homogeneous density of the 

glass. 

To solve these problems, production and tests of a variety of samples with different compositions, produced 

by different technologies, is needed to choose the ones closer to the requirements of the CBM 

experiment. Moreover, prototypes of RPCs should be constructed using the different types of glasses 

and tested in the beam to check the real performances of the detectors. 

INR started this R&D program about one year ago. During this period of time a lot of samples from 

phosphate and silicate glasses with different chemical composition and properties have been produced 

in the form of moulding with sizes 30 × 30 mm 2 and 100 × 110 mm 2 and with thickness about few mm. 

After that the samples have been grinder and polished. Then the glass resistivity has been measured. 

The resistivity of one of the sample (phosphate glass) is shown in Fig. 5.7 as function of the applied high 

voltage (upper curve) and as a function of time, with applied HV = 1 kV, (lower curve). The resistivity 

is about 10 10 Ωcm at applied voltages of about several hundred volts. 

An RPC prototype has been constructed using phosphate glass. Beam tests of a chamber of the single 

cell type with 4 gaps 0.3 mm wide (described in section 5.4.1) have been performed at ITEP PS with 

protons of 1.8 GeV/c momentum and an intensity of up to 0.5 · 10 6 particles per spill. The spill duration 

was ≈ 300 ms. The employed gas mixture was 85% C2H2F4 + 5% iso-C4H10 + 10% SF6. Results of the 

efficiency and time resolution measurements are shown in Fig. 5.8. 

Despite the degradation in the time resolution from 90 ps at counting rates of about 1 kHz/cm 2 to 120 

ps at 18 kHz/cm 2 , this result is a significant step-forward in the detector improvement. Fig. 5.9 gives a 

detailed information about the dependence of the shape of the time spectra on the rate.


Figure 5.7: Resistivity of the sample of the phosphate glass as function of applied voltage (upper curve) and as 

function of time (lower curve) measured at an applied voltage of 1 kV. 

Figure 5.8: Time resolution and efficiency of an RPC prototype made with phosphate glass, as a function of the 

beam rate. 

In addition to a rather slow degradation of the TOF resolution (rising from 90 ps up to 120 ps), one can 

see an increasing admixture of tails in the timing distributions. This is a serious obstacle preventing the 

usage of RPC for precise particle identification. The way out of this problem still has to be found. 

Preliminary results of the glass production show that the technology should be simpler for silicate glasses. 

The resistivity of the first samples 30 × 30 mm 2 of the silicate glasses doped by Fe, Ti and Ge are 

presented on Fig. 5.10. At present few samples of silicate glasses have been produced to construct other 

RPC prototypes, but beam tests have not been performed yet. Production of the first plate with dimension 

about 1000 × 200 × 2 mm 3 is foreseen for end of 2004 still under laboratory conditions.


Figure 5.9: Timing properties of the same RPC prototype used for the data of Fig. 5.8 at different rates. 

Figure 5.10: Resistivity of the sample of the silicate glass as function of applied voltage (upper curve) and as 

function of time (lower curve) measured at an applied voltage of 1 kV. 

5.3.1.3 Rate capability extension through temperature increase 

Here we describe an experimental demonstration of a considerable improvement on the counting rate 

capability of glass timing RPCs by means of a moderate warming of the detector.


It is known that the resistivity of many ionic conductors follows an Arrhenius law 

ln(ρ) = a + b 

T , 

which for narrow temperature intervals may be conveniently represented as 

ρ ρT0 10(T0−T )/ΔT , (5.1) 

where ΔT is the temperature increase required for a resistivity decrease by one order of magnitude and 

the resistivity at the reference temperature. 

ρT0 

A measurement of several flat-glass types from common brands, has shown a relatively uniform rate 

of decrease of the resistivity with the temperature by about one order of magnitude per ∼25 o C of 

temperature increase. Further details may be found in [141]. 

Naturally, as already shown experimentally by [142] for wide-gap RPCs, it is to be expected that the 

counting rate characteristics of RPCs, made from such glasses, will improve when the glass temperature 

is increased. Therefore, it is interesting to consider the use of warmed-up detectors for extending the 

counting rate capability of timing RPCs while keeping the practical advantages of using industrial flat 

glass for its construction. 

The tests were performed on one of the 2×60 cm 2 four-gap shielded timing RPCs described in [122], 

which were equipped with several internal thermometers and inserted in an external heating sleeve commanded 

by a temperature control system. An illustration of the test setup is shown in Fig. 5.11. 

The internal temperature differences were generally less than one degree measured over the RPCs aluminum 

shield, assuring that, under static conditions, the temperature of the enclosed RPCs would stay 

within the boundary temperatures. Further details may be found in [141]. 

Figure 5.11: Schematic representation of the test setup. Irradiations were made with radioactive sources of 22 Na 

and 60 Co. The 22 Na positron annihilation source allows performing timing measurements in coincidence with a 

scintillator. 

Timing accuracy 

Timing measurements were performed by coincident measurement of the photon pairs from the 22 Na 

source between the RPC and a plastic scintillator. The time resolution of the scintillator for photons was 

estimated by performing the same measurement between two identical scintillators, yielding a resolution 

of 110 ps σ.


A series of measurements were performed as a function of the source intensity, of the applied voltage 

and of the temperature. The applied voltage was rescaled to an effective value 

V ∗ 0 = V0 

T 

Tre f 

where Tre f and Pre f are a common reference temperature and pressure. The results for the timing resolution, 

together with a sample time spectrum, are shown in Fig. 5.12. Surprisingly, none of these variables 

exerts any effect on the timing accuracy for gamma photons. The observed resolution around 90 ps σ 

agrees well with previous investigations. 

Figure 5.12: Timing accuracy (ps σ) as a function of the source intensity, effective applied voltage (see text) and 

temperature. Surprisingly, none of these variables exerts any effect on the timing accuracy for gamma photons. 

For reference, some measurements were done with cosmic rays, yielding a resolution of 76 ps σ, also in 

qualitative agreement with previous results obtained with this detector [122]. 

Efficiency 

The measured charge spectra for an RPC irradiated with gamma photons is very well described by an 

exponential [141]. So in this particular case, the efficiency of timing RPCs should be very sensitive to 

variations in the operating parameters. Therefore, this quantity may be more promising than the timing 

resolution, as an assessment of the effects of temperature on the counter. However, the extraction of the 

relevant physical parameters from the data requires the development of a minimally realistic model of the 

detector efficiency. This model, described in [141], contains four fixed parameters plus one parameter 

for each temperature, in total 10 parameters in the present study. These parameters can be robustly 

determined by a global least squares fit to the data (a total of 84 individual curves), being an example 

show in Fig. 5.13. 

After fitting the model to the experimental data one can extract the relative improvement of the rate 

capability due to temperature [141] and compare it with what is expected from the direct measurement 

of the glass resistivity. These quantities are represented in Fig. 5.14. 

Based on these results, one may extrapolate measurements made with the same counter on particle 

beams [122], suggesting that operation at resolutions below 100 ps σ and efficiency of close to 90% 

may be feasible at 6 kHz/cm 2 when the detector is warmed up at 50 o C (10-fold increase in rate capability 

[122]). 

Pre f 

P


Figure 5.13: Least squares fit of the model (lines), described in [141], to the observed counting rates (points) 

as a function of the avalanche position in the central ±20 cm of the RPC. Each panel corresponds to one of the 

used sources. Within a panel, each graph refers to the temperature indicated in degrees in the inset. It is evident a 

sharp increase on the visible counting rate with increasing temperature for the strongest sources. The thicker line 

corresponds to the visible rate at zero current. 

Figure 5.14: Relative increase of the rate capability with the detector temperature, as estimated by measurements 

of detector behavior and by direct measurements of the glass resistivity. It is evident the good agreement, indicating 

a rate improvement by a factor 10 when the temperature is increased by ∼25 o C. 

5.3.2 Aging 

Severe aging of glass RPCs operating in streamer mode has been reported [143, 142] and related to the 

presence of water vapor traces in the gas mixture. An unidentified deposit was found over the glass 

surface, severely increasing the dark count rates and reducing the counter efficiency. 

Naturally it is of considerable practical importance to investigate such effects in timing RPCs, often made 

with glass electrodes and working in somewhat similar gaseous mixtures.


The test setup comprised six 9 cm 2 single-gap counters, each made with one glass (SCHOTT ATHERMAL R○ 

tainted glass) and one aluminum electrode. Three counters had a glass cathode and three had an aluminum 

cathode. Further details are available in [144]. 

Dark current 

As an increase in dark current was the main experimental indication of aging reported in [143], we use 

this parameter as indicative of an eventual aging effect. 

Fig. 5.15 shows the dark current for each of the six chambers averaged over the second of the two daily 

rest hours. There is no evidence of any systematic long term increase with time, suggesting that the 

aging effect mentioned above is less severe in timing RPCs than in streamer mode RPCs. The short term 

fluctuations are well correlated for the three chambers of the same type and are of environmental origin 

(mostly temperature). 

When the cathode is made from aluminum, the new chambers apparently need to work for a longer time 

until the dark current stabilizes. A possible explanation may be related to the different surface porosity 

of aluminum and glass, the former easily trapping microscopic dust particles that generate dark currents. 

Nevertheless there is a general tendency for the initial dark currents to decrease with time. 

Figure 5.15: Dark current as a function of operation time. No systematic increase in dark current is apparent. 

(Positive current: glass cathode; negative current: glass anode). After 280 days two counters (1), one of each 

configuration, were removed for surface analysis, and replaced by two new chambers (2). 

Integrated charge 

Fig. 5.16 shows the charge accumulated in each chamber after 480 days of operation. Two chambers were 

removed for surface analysis at day 280. The replacement chambers had the tainted glass substituted by 

ordinary sheet glass. 

Considering an average avalanche charge of 10 pC, the accumulated charge of 800 mC corresponds to 

about 80 × 10 9 avalanches in each chamber. As the effectively illuminated area in each chamber is about 

1 cm 2 , one estimates that the chambers under test operate at a counting rate of about 2.5 kHz/cm 2 . The 

charge accumulated so far corresponds to 2.5 years and 1 month of operation at a counting rate of 1 and 

20 kHz/cm 2 , respectively. 

Surface composition analysis by Electron Probe Microanalysis 

After 280 days of operation two chambers were opened for analysis. A visual inspection of the electrodes


Figure 5.16: Integrated charge as a function of operation time. Two chambers (1) were substituted after 280 days 

by new ones (2). The remaining four chambers accumulated an integrated charge of about 800 mC. 

revealed a multicolored dry deposit over the glass electrodes. The colors appeared to be merely due to 

light interference and not intrinsic to the material. 

When the glass was operated as a cathode, the deposit was well localized over an area of approximately 

1 cm 2 close to the UV-light entrance slit, presumably corresponding to the effectively illuminated region. 

When the glass was operated as an anode the deposit was distributed over most of the electrode area. 

The aluminum electrode facing the glass cathode appeared covered with a localized matter layer that coincided 

with the deposit on the glass. The aluminum electrode facing the glass anode seemed completely 

clean. 

The results of a qualitative Electron Probe Microanalysis (EPMA) of the most abundant elements found 

on the surfaces of the glass electrodes (anode and cathode) and, for comparison, on both surfaces of 

a new glass are presented in Table 5.1. Only the relative concentrations within the same element are 

meaningful and no comparisons should be made along the table columns. 

The surfaces in contact with the gas show a significant enrichment in fluor, oxygen and sulphur contents 

after irradiation, presumably by deposition from the gas (oxygen may be also extracted from the glass). 

These elements may eventually be associated with lighter ones, but the method could not detect elements 

lighter than oxygen. 

There is also some surface enrichment in elements that are not present in the gas. For certain elements 

the enrichment is observed both on glass anodes and cathodes (Si, Al, Ca, Mg) or only on one electrode 

polarity (Cl, Na, Fe). Naturally aluminum could have been etched from the opposing electrode and 

transported by the avalanches across the gap. The enrichment observed for the remaining elements could 

be a consequence of the ion flow in the glass due to the electric current. However this process does 

not explain why the enrichment appears both in the anode and cathode surfaces or why there is not a 

corresponding depletion on the back surfaces. 

A similar analysis of the aluminum electrodes is also shown in Table 5.1. There is a corresponding 

enrichment in fluor for both electrode polarities. However the aluminum cathode seems to have been 

somewhat cleaned of initial traces of oxygen and silicon, while the anode appeared enriched not only in 

oxygen but also in potassium and sodium. 

Clearly a full understanding of the ion flows in aluminum-glass RPCs is not available at this moment.


GLASS 

new new cathode anode 

Element glass glass back cathode back anode 

face 1 face 2 face face 

F 0 0 81 669 0 228 

O 438 360 505 1002 627 890 

Cl 0 0 0 0 0 84 

S 0 0 0 7 0 5 

Si 29 53 84 536 170 1052 

Al 4 7 7 55 11 44 

Mg 4 5 7 29 11 17 

Na 8 13 44 55 11 344 

Fe 84 60 78 184 96 91 

Ca 0 0 0 40 0 66 

ALUMINUM 

Element new cathode anode 

F 0 125 302 

O 99 50 133 

Si 27 9 26 

K 0 0 12 

Na 0 0 14 

Al 5827 6094 5274 

Table 5.1: Qualitative Electron Probe Microanalysis of the most abundant elements found in the surfaces of two 

glass electrodes and, for comparison, in both surfaces of a new, never used, glass. Elements lighter than oxygen 

were not detected. Units are arbitrary. Only the relative concentrations within the same element are meaningful and 

no comparisons should be made along the table columns. Similar analyses were also performed on the aluminum 

electrodes. 

Conclusions 

Aging studies were performed on six 0.3 mm gap timing RPCs made with glass and aluminum electrodes 

and operating in a mixture of tetrafluorethane with 10% sulphur hexafluoride, 5% isobutane and water at 

10% relative humidity. After a charge transfer of about 800 mC, equivalent to 2.5 years of operation at 

1 kHz/cm 2 , no systematic increase of dark current was detected, suggesting that no severe aging process 

is at work. 

However some thin deposits were found over the electrodes. An EPMA qualitative analysis suggested 

that the glass surfaces contained mainly fluor and oxygen, with enrichment in several trace metals and in 

chlorine. However a full understanding of all the perceived ion flows is not available at this moment. 

The tests are still under way to reach even higher integrated charges and probe conditions similar to the 

inner part of the CBM TOF wall.


5.4 RPC detector layout options 

Timing Resistive Plate Counters (RPCs) consist of a stack of planar electrode plates of high resistivity 

which are kept by spacers at fixed distances between 0.2 and 0.35 mm typically. Normally an anode 

is placed in the center of the stack, 2 cathodes enclose it at its outer surfaces. Operated in the right gas 

mixture under a uniform electric field of about 10 kV/mm between the electrodes the counters deliver fast 

avalanche signals which can be derived as single pulses from the anode or in differential mode between 

anode and the cathodes. 

How this working principle which can be inferred from Fig. 5.17 is realized technically depends on the 

various demands set by the experiment. With the very different rates the CBM TOF wall has to cope 

with certainly various polar angle ranges have to be equipped with different detector types. The possible 

options under consideration and the present status of their performance are described in the following. 

5.4.1 Single cell chambers 

A typical single-cell RPC design is sketched in Fig. 5.17; it actually describes a test counter (electrode 

size 50 x 50 mm 2 ) used to study the rate performance of semi-conductive glasses (resistivity 10 10 Ωcm) 

described in section 5.3.1.2. 

Figure 5.17: Schematic cut through a single-cell RPC. 

Single-cell counters with 4 or more gaps and surfaces of typically 10 cm 2 deliver easily time resolutions 

down to 40 ps at efficiencies above 99%. Adopted in size and shape to the needed granularity they are 

also shielded perfectly hence featuring minimum cross talk to neighbours. They are one of the choices 

for the innermost angular region of the planned TOF wall, provided suitable electrode material can be 

found which allows for the envisaged time resolutions in the high-rate environment. 

5.4.2 Multipad chambers 

A somewhat more economical realisation are multipad chambers: The surface of the electrode stack is 

much larger, the anode is subdivided into single pads which are read out separately. This allows for an 

easier coverage of larger areas; it is paid by enhanced cross talk between the pads. 

A multi-pad based RPC TOF system [120] is being constructed for the ALICE detector at CERN [145]

5.4. RPC detector layout options 137 

at present; a similar system could be envisaged for the larger polar regions of the CBM TOF wall. In 

ALICE the RPC shell will provide particle identification for the bulk of charged particles produced in 

the central rapidity region (|y| ≤ 1) at intermediate momenta (up to a few GeV/c) at particle rates below 

1 kHz/cm 2 . 

The basic design features of an ALICE multigap resistive plate chamber (MRPC) [146] are depicted in 

Fig. 5.18. 

Figure 5.18: Cross-section view of an MRPC strip used in the ALICE TOF system. 

Each module (called strip) is 10 cm wide and 120 cm long, carrying 96 rectangular anode pads of 2.5 x. 

3.5 cm 2 area (cf. the photo in Fig. 5.19). 

They are placed in the middle of the stack, the two cathodes on the external surfaces; a differential high 

voltage is applied. For the readout a differential signal is derived from anode and cathode pickup pads. 

The stack on each side has 5 gaps (10 gaps in total), each gap being 250 µm wide. The electrode plates 

are made of commercial soda-lime glass, 400 µm thick for the internal plates and 550 µm thick for the 

external ones. The spacers are made of nylon fishing line. 

Fig. 5.20 shows the efficiency, time resolution and streamer probability as a function of the high voltage. 

The efficiency reaches 99.9% at a time resolution of 40 ps and has a plateau for streamer free operation 

that is more than 1.5 kV wide. 

Fig. 5.21 shows typical time distributions before (σ = 91 ps) and after (σ = 48 ps) slewing corrections. 

Ten such strips with the final design characteristics have been tested and the results are shown in Fig. 5.22. 

All strips can be operated at 12 kV with a time resolution between 40 and 50 ps. The main disadvantages


Figure 5.19: MRPC strips layout. 

Figure 5.20: Efficiency, time resolution and streamer probability of MRPC as a function of the high voltage. 

of MRPC is the cross-talk between neighboring pads and the degradation of the time resolution near pad 

borders. Fig. 5.23 illustrates the charge induced between neighboring pads. The cross-talk probability 

and time resolution close to the pad border are shown in Fig. 5.24. 

The use of MRPC is also limited due to the high glass resistivity [120]. Fig. 5.25 shows their rate capability, 

demonstrating that RPCs of this type can be used if the count rates are not exceeding 1 kHz/cm 2 .


Figure 5.21: Time distribution of MRPC before (above) and after (below) slewing corrections. 

Figure 5.22: Efficiency and time resolution versus applied high voltage for 10 MRPC strips. 

Figure 5.23: Charge induction between neighboring cells of MRPC strips.


Figure 5.24: Longitudinal scan along the MRPC strip between two pads (◦ - left pad, △ - right pad) at 12.5 kV: 

(a) efficiency; (b) time resolution; (c) OR probability; and (d) AND probability. The distance is relative to the 

border between the two pads. 

Figure 5.25: TOF efficiency as a function of beam rate for two versions of MRPC strips.


5.4.3 Single strip counter 

A major challenge for timing RPC operation at high fractional occupancy concerns the inter-channel 

crosstalk that arises from the coupling of the GHz-bandwidth RPC pulses to the neighbouring detector 

channels. To address this issue we studied a prototype for time of flight measurements over large areas 

and at high occupancies. The detector was constituted by three independent cells placed into grounded 

aluminum boxes. Each 2 × 2 × 60 cm 3 box contained a 4-gap glass-aluminum RPC. 

Measurements were performed at the GSI (Darmstadt) SIS accelerator using fragments generated by 

primary collisions of C at 1 GeV/u. 

At 200 Hz/cm 2 the counters have shown a time resolution of 60 ps < σt < 80 ps with 3σ-tails below 8%. 

An inefficiency of 13%, compatible with the detector geometry, was observed, calling for a two-layer 

design. High mechanical robustness and homogeneity of the physical properties along the longitudinal 

direction of the detector were also observed. A measured cross-talk level of only 0.4% has shown no 

influence at all on the timing resolution. 

A procedure for the stand-alone calibration of the detector using redundant information is proposed, 

taking advantage of the very good spatial uniformity observed. 

Further details about these results may be found in [147, 122]. Efforts supported by the 6th European 

Framework Program are underway for a large scale test of this detector concept, in close cooperation 

with the upgrade program of the TOF Wall of the HADES experiment at GSI. 

5.4.3.1 Cross-talk in high occupancy environments 

Economic considerations on the design of this type of detector generally suggest that the occupancy per 

individual readout cell Pocc should be at the level of 20% in the most unfavourable scenario. Roughly 

speaking, once there is a hit in a given cell, the probability of having a hit in at least one of the neighbours 

is Nneighbours × Pocc. If these ’neighbour hits’ interfere with the ’interesting’ hit (cross - talk) the 

performance of the detector may be deteriorated for a large fraction of the hits, potentially introducing 

complex systematic effects on the measurements. 

5.4.3.2 Shielded RPCs 

To avoid potentially troublesome cross-talk, we designed and tested the shielded RPC layout shown in 

Fig. 5.26, resembling the original idea suggested in [148]. The chambers are constituted by 2x60 cm 2 , 

2 mm thick, aluminum and glass electrodes, the later being left electrically floating [149] for easier 

construction. Each RPC cell is shielded in grounded aluminum boxes (for details see [147]). 

Figure 5.26: Schematic representation of the shielded RPC prototype.


5.4.3.3 Physical environment 

Measurements were performed at the GSI (Darmstadt) SIS accelerator using fragments generated by 

primary collisions of C at 1GeV/u. For the reference time we used a scintillator system of resolution 

equal to 35 ps σ, which allowed to precisely measure the intrinsic resolution of the RPC and also the 

γβ distribution of the incoming particles, yielding the energy deposition distribution (Fig. 5.27). It 

could be estimated that at least 5% of the particles deposited at least a factor 2 more energy than the 

minimum ionizing ones [147]. However, no significant difference was observed when studying slow and 

fast particles separately. 

Events 

600 

500 

400 

300 

200 

100 

GEANT+URQMD (CC 1GeV/u) 

MEASURED 

0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2 

β 

/hfill 

Events 

120 

100 

80 

60 

40 

20 

10 -1 

dE 

(a.u.) 

dx 

1 10 

Figure 5.27: Distributions of β and γβ measured with the trigger scintillators, compared with the ones obtained 

from the HADES simulation toolkit [150]. Also shown superimposed the Bethe-Bloch curve for fluorine in arbitrary 

units. 

5.4.3.4 Cross-talk 

Despite previous experience of cross-talk levels around 85% between neighbouring strips in long detectors 

[151], we observed an excess of events of only 0.4%/neighbour due to the adequate shielding. 

Expectedly, the probability that a signal induces cross-talk is higher for the larger signals (mostly from 

streamer discharges) (Fig. 5.28). 

However the definitive test is to measure the possible degradation of the time resolution performance in 

the case where there are two almost simultaneous hits in neighbouring cells. Due to the low multiplicity 

of carbon collisions the probability of having ’neighbouring hits’ was only 1.2%/neighbour. However, a 

sample of around 1300 of such events was collected, being the corresponding, unaffected, time distribution 

shown in Fig. 5.29. It is also shown that there is no correlation between the time difference between 

both hits and the time measured in the ’interesting’ channel. 

5.4.3.5 Behaviour with rate 

The behaviour of the most relevant quantities is shown in Fig. 5.30: the dependence with the local 

rate R[Hz/cm2 ] is found to be linear and yields for the efficiency: ε = 87 − 1.5·R 

100Hz/cm2 [%], for the time 

resolution σt = 67+ 4.7·R 

100Hz/cm2 [ps] and for the tails 3σ-tails = 6.7+ 0.6·R 

100Hz/cm2 [%] (3σ-tails stands for the 

fraction of events out of 3 σ from the maximum). 

RPCs similar to the ones used are known to yield efficiencies up to 99.5% [152], suggesting that the 

measured efficiency was dominated by geometric losses. The observed intrinsic inefficiency, 13%, is 

compatible with the 15% expected for perpendicular particle incidence (1 − ε=dead zone/active zone 

3 mm/20 mm 15%). This intrinsic inefficiency can be compensated by a 2-layer scheme of the detector 

γ β


Figure 5.28: Fraction of cross-talk and coincident hits in neighbour cell (right axis) as a function of the fast 

charge in main RPC (left axis). The double hit probability does not show any significant dependence with charge, 

as expected, whereas the probability of cross-talk is much higher for streamers. 

Figure 5.29: a) Time distribution in one RPC in presence of an almost simultaneous hit in a neighbouring cell 

(between parentheses the resolution after subtracting the intrinsic resolution of the scintillators). b) No correlation 

between the time measured in the main counter and the time lag between both hits is observed. c) Distribution of 

the position of hits in the neighbour cell whenever the main cell has been fired, showing a clear peak at trigger 

position (0 cm) that can be attributed to local production of particles. Subtracting these events, the sample is 

dominated by coincident pairs coming from the target. 

layout. It might be advantageous, providing as a benefit easier mechanical construction and redundant 

information, useful for calibration purposes. 

5.4.3.6 Homogeneity and calibration 

The detector has shown good homogeneity along the longitudinal dimension (Fig. 5.31). The position 

resolution was measured to be below 6 mm σ, with systematic shifts of the distribution kept below 2.5 

mm. These facts demonstrate the mechanical robustness of the design and the negligible effect of the 

nylon monofilaments used as spacers [147]. 

In timing RPCs the measured time must be corrected for the time-charge correlation that is always present 

[151]. Additionally, in a system, the isocronicity of all time signals must be guaranteed by adequate 

inter-comparisons. The ensemble of all these procedures we call calibration. In practice this operation 

must be performed with some periodicity, since the detector-related time lags are likely dependent on


Figure 5.30: Plot showing the global behaviour with rate of the efficiency (right axis), time resolution (left axis) 

and 3-sigma tails (right axis). The 13% inefficiency at low count rates is of geometric origin, calling for a two-layer 

approach 

environmental conditions. 

In the present case it was found that: 

1. The time-charge correlation is well described by a simple two-segment linear fit in time vs. log(Q) 

representation. This can be particularly interesting for on-line purposes. 

2. The parameters of the fit can be obtained from any single position along the detector and used 

elsewhere. 

3. The time measured with two independent RPCs can be used to determine the parameters of the fit 

without the help of any auxiliary detector; we call this concept self-calibration and describe it in 

section 5.4.3.7. 

5.4.3.7 Self-calibration 

The time difference between two independent RPCs placed close to each other and measuring the same 

particles is a function of both RPC charges only (Fig. 5.32). To derive the time-charge calibration for 

RPC1, for instance, we keep only a narrow range of Q2 close to the charge distribution maximum and fit 

the points Δt(Q1,Q2) by a mosaic of two planar surfaces, yielding the required linear correction segments 

for RPC1. 

For the data shown in Fig. 5.31 the second RPC was simulated by data taken from an independent run at 

a single location chosen arbitrarily. 

5.4.4 Multistrip counter 

In analogy to the transition from a single-cell to a multi-pad counter one can also replace the single anode 

strip by a multistrip anode [153]. Such an anode (see Fig. 5.33) has a segmented strip/gap configuration; 

by reading out both ends of each strip, the ToF is determined by the mean timing, the position along


Figure 5.31: Comparison between different calibration methods in several points along the detector. Standard: 

run-by-run fit in 15 linear segments. 2 piece linear: run-by-run fit in 2 linear segments. Self-calibrated: 2 piece 

linear fit derived by self-calibration (see 5.4.3.7) from a point chosen arbitrarily. 

t1−t2 (ps) 

log(Q2) 

2000 

1500 

1000 

500 

0 

−500 

−1000 

−1500 

2.5 

2 

1.5 

0 0.5 1 1.5 

log(Q1) 

2 2.5 3 3.5 

Figure 5.32: Example of a self-calibration fit in two planar surfaces. The reference chamber corresponds to Q2 

and the fit parameters are calculated for chamber 1 (see 5.4.3.7). 

the strip is delivered by the time difference. The advantage of such a design in comparison to a pad 

like structure is the reduction of electronic channels and a potentially better spatial resolution. Of course 

the limit for this detector type is the number of hits which can be detected within an event (Multihitperformance). 

Equipped with normal floating glass electrodes such detectors offer an ideal solution for 

the outer 75 % of the total TOF wall where the rates stay below 1 kHz/cm 2 . 

As single-strip detectors such multistrip detectors need to be arranged in a staggered configuration in order 

to guarantee a full geometrical efficiency. The number of strips and the pitch are of course depending 

on the required spatial resolution and the multihit capability. Detectors of this type are presently being 

developed for the FOPI-ToF upgrade project; they are part of an R&D program which is supported by 

the European Union (JRA 12). In Fig. 5.34 a full size supermodule of the FOPI-system is shown. Five 

RPCs (each 90 cm long, 4.6 cm wide) with 8 gaps of 250 µm are placed inside a common gas and high


Glass 

z 

HV 

Multistrip anodes 

HV cathode 

¤£¤£¤£¤£¤£¤£¤£¤£¤£¤£¤£¤£¤£¤£¤ 

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y 

¡ 

Spacers 

Figure 5.33: Structure of Multistrip - Multigap RPC 

voltage box. They are staggered with an overlap of 3 strips between the front and back layer RPCs thus 

giving full geometrical coverage. A 16 strip anode with a strip to gap ratio of 1.6/0.94 mm has been used 

to optimize the cluster width and impedance adaption. The detector is read out via a multipin connection 

system [154], the sockets of which are soldered directly on the anode board and the input card of 

the Front-End-Electronic (FEE). The connection is made by a flexible and low pitch (0.8 mm) 50 Ohm 

multi-coaxial cable. The electronics is placed directly behind the detector box; it comprises the FEE part 

with the preamplifier and discriminator and a TAC-based readout card (TAQUILA) which digitizes and 

acquires the data of 16 channels of one side of a detector (i.e. 10 of these systems are placed behind the 

box). 30 of such supermodules with in total 150 MRPCs and 4800 electronic channels will be used in 

the FOPI project. 

In-beam tests the detectors have demonstrated time resolutions of σt ≤ 100 at rates up to 1 kHz/cm 2 , cf. 

Fig. 5.35; the non-gaussian component of the time distribution is below 2 %. 

The dependence of the resolution on the applied high voltage is shown in Fig.5.36. The efficiency rises 

within a few kV to 99 %; at the same time the resolution improves constantly, levelling off at about 60 

ps. In addition the rate dependence has been studied: Whereas no influence on the efficiency has been 

seen between 100 Hz/cm 2 and 1000 Hz/cm 2 , a degradation of the time resolution from σt ≤ 60 to 90 ps 

is visible in the plot. Nevertheless one can conclude that a multistrip counter can safely be operated at 

rates up to ∼ 1kHz/cm 2 with time resolutions below 100 ps. 

The second important issue of such a detector is the multihit capability. For this purpose the shown 

module was placed in the final experimental position in FOPI in order to test its timing and efficiency 

performance in a realistic experimental multihit environment. As can be seen in Fig. 5.37 the efficiency 

drops form 99% to 85%, which simply reflects the fact that an active strip can not count a second hit 

within the same event. On the other hand the time resolution deteriorates from 80 to 100 ps for the 

second hit. 

As mentioned, the multistrip-multigap RPC is comparable to single cell or pad read-out devices in terms 

of time resolution and counting rate performance. However, it has the further advantage of delivering 

position information (∼15 mm along the strips and ∼ 200-300 µm across the strips). This feature recommends 

such a type for large-area TOF systems where the particle density leads to a negligible multihit


Figure 5.34: Photograph of prototype counters. Five counters share the same gas volume box. 

Figure 5.35: Time resolution measured with multistrip prototype 

probability within the cluster. On the other hand the position resolution could be of interest for tracking 

also in the high rate enviroment. 

In further investigations it is planned to replace the present float glass electrodes (10 12 - 10 13 Ωcm) by 

float glass of lower resistivity in order to conserve the present perfomance also in a high counting rate 

environment. Such glasses would combine a low resistivity with the surface and evenness properties of


Efficiency (%) 

100 

80 

60 

40 

20 

0 

100 ps → 

75 ps → 

60 ps → 

New wiggle 

Efficiency 

← 100 % 

← 95 % 

σ t - σ ts 0.8 ∼ 1 kHz / cm 2 

σ t - σ ts ∼ 0.1 kHz / cm 2 

σ t - σ ts ∼ 0.1 kHz / cm 2 

10 10.5 11 11.5 

Voltage (kV) 

12 12.5 13 

Figure 5.36: Time resolution and efficiency as function of applied voltage for a 8-gap counter 

Efficiency (%) 

100 

80 

60 

40 

20 

0 

150 160 170 180 190 200 210 220 230 240 250 0 

HTYP 

Full-RPC 

Mini+RPC 

Figure 5.37: Double hit time resolution 

the standard window glass - at a higher prize of course. A possible solution is Glaverbel float glass [155] 

with a resistivity of the order of ∼10 10 Ωcm. Prototypes equipped with such glass electrodes have been 

built already; except for the glass they are identical in size and structure to the standard counters, which 

should allow for a meaningful comparison of their features. 

Eff. 

σt-σts Eff. 

σt-σts 200 

180 

160 

140 

120 

100 

80 

60 

40 

20 

0 

175 

150 

125 

100 

75 

50 

25 

σ t (ps) 

σ t (ps)

5.5. RPC electronics 149 

5.5 RPC electronics 

The time of flight measurement poses completely different demands on the readout system than the other 

subdetectors. The signal amplitude is only of interest for time correction issues, so the demands on the 

amplitude resolution are relatively modest. The demands on the time resolution however are extremely 

high. To get a sufficient kaon identification efficiency, a time resolution better than 80 ps for the complete 

system is required. The event-rate per channel will be in the region of 100 kHz. 

To reach these timing requirements with the foreseen RPC detector, a highly optimized time measurement 

electronic has to be developed. The experience with an existing readout system for a RPC detector at 

GSI which is described in section 5.5.1 could be a good starting point for this development. 

5.5.1 FOPI-RPC-Electronics 

The major issue of the FOPI readout electronics is a ToF-resolution of σt ≤ 100 ps for the complete 

RPC-barrel consisting of 5000 channels. For this purpose a 16 channel Front-End-Electronic card (FEE) 

with a gain of α ∼ 200 at a bandwidth of δ f ∼ 1000 MHz was developed. This card has a single ended 

50 Ω input stage and two differential (PECL) outputs per channel for timing and charge. 

The analog amplification is realized in three stages with a maximum gain factor of 10 per stage. Discrimination 

of the timing signal is also done on this FEE-card. With the mentioned gain of 200 at a low 

preamplifier noise (≤ 40 mV) we are able to measure primary RPC signals above 0.4 mV. 

The FEE-card is directly connected to the digitization part which contains a 16 channel common stop 

TAC-system followed by the readout part of the Data Acquisition (DAQ) [156]. This so called TAQUILAcard 

uses a single channel TAC-ASIC, designed at GSI, which is based on a 0.8 µm CMOS-technology 

and described in section 5.5.3.1. A typical time resolution between two channels on one TAQUILA 

board without a FEE card is about 10 ps. After correction of nonlinearities 7 ps RMS are reachable. The 

clock distribution over a distance of more than 10 m and up to three stages shows a RMS jitter of about 

5 ps. 

From pulser tests we determined the system resolutions as follows [121]: 

System σt (ps) 

1 TAQUILA 12±2 

1 FEE + 1 TAQUILA 26±3 

2 FEE + 2 TAQUILA 33±4 

Subsystem Resolution (ps) 

FEE σFEE ≤ 18 ± 3 

TAC σTAC ≤ 12 ± 2 

Clock shift σCLK ≤ 15 ± 3 

Card variations σCARD ≤ 20 ± 3 

Sum σtotal ≤ 33 ± 4 

The results of this test showed that our new developed electronics has a multi-card resolution of σtotal ≤ 

33±4 ps. From this we can conclude that the electronics will not limit the timing measurement of the 

RPCs. One can deduce further that the clock cascading system, which was used for the first time in this 

test, has a minor effect on the total resolution (σCLK ≤ 15±3 ps).


5.5.2 Preamplifier/discriminator 

The Resistive Plate Chamber (RPC) is a gaseous detector which can deliver very fast pulses when an 

ionization particle passes through. Typical characteristics of detector signals for 50 Ohms impedance of 

the detecting strip are presented in Table 5.2. The amplitude pulse height spectrum is continuous with a 

peak value in the range of 2-5 mV. 

Parameter Value 

Rise Time 0.3 ns 

FWHM 1-2 ns 

Fall Time 0.3 ns 

Amplitude (maximum) 30 mV 

Table 5.2: Typical characteristics of RPC signals for 50 Ohms impedance. 

REFE. SIGNAL 

START 

PARTICLE STOP DELTA T INFORMATION 

OUT 

DETECTOR AMPLIFIER DISCRIMINATOR TIME DIGITTZER 

Figure 5.38: A typical time measurement channel. 

Due to its fast response, the RPC Detector can be used for Time of Flight (ToF) measurements, and in 

this case, the associated electronics must process these fast and low amplitude pulses with the minimum 

amount of supplementary time errors. A typical time measurement channel is shown in Fig. 5.38. 

The amplified and leading edge discriminated signal starts the time digitizer and the reference signal 

stops it. The output signal contains the useful information Δt, the time difference between Start and Stop 

signals. In our concept, the amplifier and discriminator blocks are included in the FEE ToF preamplifier 

unit. 

Fig. 5.39 shows the main error sources in time measurements: 

1. ‘WALK’ is the error due to amplitude variation of the input signal. If the rise time is constant, 

the time needed to reach the threshold value level depends of the input amplitude; this error is 

systematical and can be corrected if the information regarding the amplitude of the input pulses is 

known or if the discriminated signal has a width dependent to Time over Threshold. 

2. ‘JITTER’ is a random error due to noise of amplifier. The peak-peak noise signal projected on 

time scale by the slope of signal give the jitter peak-peak value. To reduce the jitter, the ratio noise 

to slope must be as small as possible. 

The parasitic signals coupled from outside to measurement channel increase the jitter. To decrease this


UIN D 

VTHR 

UOUT D 

TSTOP 

"WALK" 

t 

t 

UIN D 

VTHR 

6SIGMA A 

UOUT D 

TSTOP 

6SIGMA T 

CLEAN SIGNAL APPLIED TO DISCRIMINATOR NOISY SIGNAL APPLIED TO DISCRIMINATOR 

Figure 5.39: A schematic illustration of walk and jitter errors. 

influence, the CBM FEE should be oriented to differential coupling to the detector. A low cost solution 

can use twisted pair cable giving a moderate rejection of parasitic pick-up. 

The time over threshold walk correction can be favorable in the high integration case because it needs 

two time values instead of time and amplitude information. 

We have some experience in FEE ToF from FOPI experiment, and in this moment we consider that the 

main initial technical parameters for CBM FEE ToF preamplifier are: 

• Input impedance: Differential with 50 Ohms to ground of each line 

• Gain: >100 

• Bandwidth: >1 GHz 

• Noise, related to input:


SIGMA [ps] 

100 

10 

1 

THR=100mV 

THR=12mV 

TIME RESOLUTION 

FEE1 Ch3 

1 10 100 

UINP [mV] 

Figure 5.40: ToF resolution as a function of signal amplitude. 

5.5.3 Time Measurement and Digital Backend 

In the existing RPC readout system mentioned in section 5.5 a time to amplitude converter (TAC) is the 

central element for the time measurement. It provides a time resolution of 7 ps between two channels 

on the same 16 channel readout PCB and about 20 ps between two channels on different PCBs. This 

time resolution would also be sufficient for CBM. An amplitude correlated value, which is needed for 

walk correction of the measured time information, could be received from a simple time over threshold 

measurement. 

As a second variant, a Delay Locked Loop (DLL) based time measurement system will also be evaluated. 

5.5.3.1 Time Measurement by Using a Time to Amplitude Converter 

Figure 5.41 shows the block diagram of a TAC based readout system for the CBM time of flight measurement. 

A TAC core is used to measure the time interval between a signal coming from the detector 

and the next rising edge of a reference clock. Therefore the start input of the TAC is connected to the 

signal input and the stop input is connected to the reference clock. 

While the input signal is active, a time over threshold measurement is done by a simple integrator. The 

output signals of the time to amplitude converter and of the time over threshold integrator are converted 

into digital values by two ADCs. 

For the self triggered DAQ, which is supposed to be used in the CBM experiment, a time stamp for every 

local event is needed. This time stamp is latched from a time stamp counter with the incoming detector 

signal. All these values are stored in an event fifo memory. The DAQ-Interface reads the data from this 

fifo and arranges it in event messages that are sent to the DAQ network by a serial interface. 

The time to amplitude converter core, which is shown in figure 5.42, is based on a distributed RC network 

which is fed via tristate drivers by a digital delay chain. The delay chain consists of a linear arrangement 

of delay cells, which contains two inverting stages as shown in the dashed box on the right side. For the 

first inverting stage a nand gate is used to provide a common reset input. 

A start signal is latched in a d-flipflop and then traveling through the delay chain and connecting one


Reset 

Start 

Stop 

Running 

In 

Clk 

High 

D 

D 

R 

5ns 

R 

Q 

Q 

Q 

Q 

Reset 

In 

Reset 

Start 

Stop 

Time over 

Threshold 

Running 

Latch 

Clock Epoche 

Reset Set 

Timestamp Counter 

Out 

Time to 

Amplitude Out 

A 

D 

C 

A 

D 

C 

Event 

FIFO 

Write Read 

Controller 

Figure 5.41: block diagram of a TAC based readout system 

... 

D D D D D 

R R R R R 

C 

Out 

Delay cell 

Figure 5.42: principle diagram of the time to amplitude converter core 

Reset 

tri state 

Out 

D 

A 

Q 

- 

I 

n 

t 

e 

r 

f 

a 

c 

e 

In Out 

resistor of the RC network after the other to supply voltage. So, while the signal is traveling through the 

delay chain, the charge in the capacitor is increasing linearly with time. 

After a delay of 5 ns, which prevents the TAC stopping directly after starting, a clock latch d-flipflop is 

enabled, so the next rising edge of the clock signal is stoping the conversion by setting all tri-state drivers 

to high ohmic state, so the RC network is disconnected from ground and supply voltage and the capacity 

will keep the current charge. With a second RC network which is complementary fed to the shown one 

a differential output could be provided to double the signal swing. 

The GSI TAC ASIC in the existing RPC readout is carried out in a 0.8µm CMOS process. To investigate 

the properties of a delay chain in a later technology some simulations based on the AMS 0.35µm 

CMOS process have been done with a delay cell that was optimized for minimum propagation delay. 

To consider parasitic capacities also a layout for this cell was done (shown in figure 5.43) and extracted 

for the simulations. The simulations show that one could expect a delay of 95 ps per delay cell with a 

temperature coefficient of 0.18 %/K. 

For the clock frequency a value of 51.2 MHz leading to 512 (= 2 9 ) clock cycles in a 10µs period seems 

to be reasonable. In this case the delay chain would have to cover the 19.5 ns clock time period, the 5 ns


Q 

MN6 

R RN In RNMP0 

R 

MN1 

MN0 

MN2 

Out 

MP1 

MP2 

Figure 5.43: layout of the simulated delay cell 

MP3 

delay and some safety margin, so at least a 25 ns coverage would be needed. This would lead to a delay 

chain of 270 delay cells. 

From the experience with the existing time to amplitude converter and these first simulations it is obvious 

that also for the CBM RPC detector a high precision TAC based time measurement is feasible, but there 

are still open questions which need some investigation in the next time. 

During operation of the TAC the current in the RC network reaches for some nano seconds up to 16 mA. 

This causes a voltage drop of several mV of the supply voltage on the chip. If more than one channel 

is implemented on the same die much care on decoupling the power supplies has to be taken to avoid 

interferences between the channels. However, the best way to do this is not clear yet. 

Noise, induced by the components of the digital backend on the chip, could also influence the performance 

of the time measurement. Due to the fact that this is correlated noise the choice of an optimized 

phase shift between TAC clock and digital clock could help keeping this influence small, but this has to 

be investigated. 

A very challenging part of the readout chip is the ADC. At a TAC time coverage of 25 ns for a sufficient 

time quantization a 12 bit ADC is needed. The demanded conversion time is given by the typical event 

rate per channel. A conversion time of 1µs seems to provide enough safety margin for a 100 kHz event 

rate. 

The next step should be the design of a test structure that should contain the following blocks: 

• At least two time to amplitude converter cores to investigate crosstalk and power supply decoupling 

• A clock distribution network for clock skew and jitter measurement 

• A digital logic block with a programable clock phase shift for noise measurements 

• The first iteration of a 1µs 12 bit ADC 

Modern deep sub micron technologies provide for complex digital logic several benefits like lower power 

consumption, higher speed, higher number of metal layers, etc. On the other hand process technologies 

with larger feature sizes are much cheaper and work with a higher core voltage which is very helpful for 

analog designs like the time to amplitude converter and the ADC. For this reason at the moment a medium 

scale technology like 0.35µm CMOS seems to be the most reasonable for the TAC implementation.


5.5.3.2 Time Measurement using a Delay Locked Loop based TDC 

A second design solution for ToF could be a time measurement based on a DLL (or PLL). The principle 

is shown in figure 5.44. The core of a DLL circuit is a delay chain made up of N identical delay elements 

with adjustable delay. A reference clock signal is injected into the delay chain. The delays are adjusted 

by a regulation loop such that the output signal exactly coincides with the reference clock. Each delay 

element is guaranteed in this way to have a delay of 1/Nth of the reference clock period. A PLL uses 

a ring oscillator made up of N identical delay elements with adjustable delay. One element must be 

inverting so that an oscillation with a period of 2N is maintained. The frequency of this oscillation (or a 

down-scales signal with lower frequency) is locked to a reference clock. Both systems offer the advantage 

of an absolute calibration of the time steps in all channels w.r. to the reference clock. Upon arrival of a 

hit, the status of the delay elements is latched and decoded. By implementing a suited buffering scheme, 

the circuit is immediately ready to process a new hit. A second advantage of this approach is therefore 

the very small dead time after a hit. 

Figure 5.44: Block diagram of a Delay Lock Loop 

In general the achievable resolution for a DLL is limited by the minimum gate delay which is given by 

the used technology. The resolution for a simple N element DLL scheme is determine by the intrinsic 

delay of a basic cell, consisting of 2 inverter stages, with Tn = T 

N , were T is the system clock period and 

N the number of basic cells. A rough estimation concerning this intrinsic delay for the UMC 0.18µm 

process yields about 70ps for one delay element. Due to the extremely high receivables for the time 

resolution of the RPC detector different DLL based schemes are possible. 

Several design variants have been proposed to optimize among the parameters speed, tuning range, linearity, 

power consumption, layout area etc. Some design aspects are: 

• Use single ended or differential delay elements. Single ended implementations often use currentstarved 

inverters. To keep the delay of rising and falling edges exactly equal, units of two such 

inverters form one delay element. This increases the minimal delay and degrades the time resolution. 

Differential logic is very popular for these circuits, because the inverse of the signal is 

readily available so that only one differential circuit per delay stage is required. As an example, 

we have measured a delay of < 150ps per stage at a power consumption of ≈ 100µW in a 0.35µm 

technology. Constant current logic produces much less spikes in the supply lines than CMOS so 

that a better performance my be achieved. 

• An improved time resolution can be obtained by running an array of DLLs [157] with controlled 

phase shifts (see Fig. 5.45). Here the time resolution is determined by the number of delay elements


N and the counts of M DLL’s with Tm = T 

N ˙M . 

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¤¡ 

¢¤¦¤ 

¢¡¤¤¤¤¤¡¤¥ 

Figure 5.45: An array of Delay Locked Loops 

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¤¤¢ 

• The delay line taps or the input signal can be delayed by small time steps to get a sub-unit-delay 

resolution (Fig. 5.46). By determining in which sample the rising edge of the system clock appears 

on a delay cell output one can calculate the arrival time of the event with the resolution of the 

sample interval. 

• Analog methods could be used to interpolate between successive transitions. 

Several advantages of the DLL based TDC should be pointed out: At first the structure allows for self calibration 

to compensate temperature and process variations. A second point is a dead time free operation 

that allows to cope high event rates. 

The basic building blocks required for the realization of this concept (controllable delays, interpolation, 

frequency division, phase comparison, charge pump for regulation, latches, r/o logic) will be evaluated 

in a first step. Feasible implementations to achieve the required specifications will then be designed. 

5.6 TOF detector test facility 

Detectors tests need minimum ionizing particles (MIPs) in most cases, either protons or secondary beams 

(e.g. pions) which are focussed directly on the detectors; as an alternative ejectiles from nuclear reactions 

can be used. Usually the intrinsic time resolution of the beam is quite broad. Hence in most beam tests 

reference counters are needed; ideally they should have time resolutions comparable or better than that 

of the investigated counter itself. The measured time has then to be corrected for the resolutions of 

electronics and reference counters; moreover the electronic walk has to be corrected for. The latter is 

even more critical in case the reference counters exhibit an electronic walk, too.

5.6. TOF detector test facility 157 

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Figure 5.46: Time interpolation circuit [158] 

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If, however, the test beam itself has a very good time structure, one may avoid in an elegant way many of 

the mentioned problems by referencing the test detector against the beam signal itself. This is the case 

at the Rossendorf electron accelerator ELBE which provides electrons up to 40 MeV in beam packets 

with a time spread of better than 10 ps. Moreover, electrons with energies of a few 10 MeV have specific 

energy losses very similar to MIPs, thus being suited to test the counter behaviour in terms of pulse 

height, time resolution and detection efficiency. 

coincidence rate (Hz) 

100 

90 

80 

70 

60 

50 

40 

30 

20 

10 

0 

15 MeV e - on Al (18μm), I=22nA 

-20 0 20 40 60 80 

angle relative to 45 deg 

Figure 5.47: Background distribution relative to 45 degrees


The challenge of this elegant method is to provide electron beams of intensities down to ∼ 1 kHz/cm 2 . 

Currently, beam diagnostics at ELBE requires a minimum beam intensity of ∼ 1µA or ∼ 1 × 10 10 

Hz/cm 2 . In tests intensities down to ∼ 1 nA have been reached, but this rate is still too high for inbeam 

detector tests. While it is conceivable that even lower intensities might be available in the future, 

the present solution is to reduce the intensities by scattering from a target. In this way one can reduce 

the intensity to any desired value; at the same time one can also vary the size of the irradiated spot on the 

detector. 

First parasitic beam tests were performed in the dedicated radiation cave at ELBE. A 17 MeV electron 

beam was scattered off a thin Al foil. Electrons were detected under 45 degrees scattering angle. The 

background distribution was scanned with respect to the main scattering direction, as shown in Fig. 5.47. 

A second test will use scintillators with very good time resolution as they have been used in RPC tests in 

other laboratories. 

The experimental environment has been prepared as a general setup for in- and external users (hardware, 

gas supply/mixing systems in the cave and adjacent laboratories, cable connections, etc.). A second 

irradiation station for detector tests is in preparation in another cave. 

5.7 Infrastructure 

The infrastructural requirements are largely defined by the power needs of the fast electronics operated 

on the detector. A conservative estimates is 1 W/channel of thermal power generation, e.g. 80kW of 

power have to be cooled by a common system. The gas system is considered an integral part of the 


Mounting tools (crane) have to be installed to facilitate mounting and servicing of detector modules at a 

height of 11 m above ground.

5.8. Working packages and milestones 159 

5.8 Working packages and milestones 

The main working packages and the anticipated time periods are listed in Table 5.3. The main milestone 

are the Technical proposal in 2006 and the Technical Design Report for the RPC subsystem that should 

be doable by end 2009. 

activity time period 

Material tests with single cell RPCs 2005 - 2006 

Optimisation of operation parameters (temperature, gas mixture, 

HV, ... ) 

2005 - 2006 

Test of Pad/Multipad/Strip/Multistrip Layouts 2005 - 2006 

Design and test of FEE prototypes 2005 - 2006 

Design and test of TDC prototypes 2005 - 2006 

Performance simulations with global tracking 2005 

MC simulation for optimisation of wall layout 2006 


design and test of detector modules 2007 - 2009 

design of support structure 2008 - 2009 

design and test of discriminator 2007 - 2009 

design and test of digital readout 2007 - 2009 

final choice of ASIC technology 2009 

technical design report end 2009 

production, installation, tests 2010 - 2012 

5.9 Cost estimate 

Table 5.3: RPC future planning 

At this time the cost estimate for the RPC subsystem can only be based on scaling arguments from 

existing RPC system designs [120,121]. Due to unknown choices of material and electronics technology, 

large uncertainties exist. Therefore in Table 5.4 ranges for the various cost items are given for the final 

detector components. 

Item Cost (kEuro) 

Glass plates 250 - 1000 

Cables 250 - 400 

Detector modules 500 - 700 

Mechanical support 200 - 300 

FEE electronics 1000 - 1500 

Digitizer and clock 2000 - 2600 

Gas system 150 - 200 

Services (LV/HV/cooling) 600 - 700 

Total 5000 - 7400 

Table 5.4: RPC cost estimate

160 Resistive Plate Chambers (RPC)

6 Electromagnetic Calorimeter (ECAL) 


The calorimeter system of CBM will be used for the identification of electrons and photons and the 

precise measurement of their momenta. Been used together with RICH and TRD, the electromagnetic 

calorimeter (ECAL) will significantly contribute to the particle identification due to its high capability to 

discriminate photons, electrons and hadrons. 

The ECAL will also allow a reconstruction of neutral mesons decaying in their photonic decay channels. 

The precise measurement of mass and width of short-living mesons (ω(782), η ′ , f2(1270), etc) will shed 

light on the chiral symmetry restoration which is expected to occur in dense nuclear matter. A measurement 

of the π 0 spectrum is important to study the dependence of the particle yield on thermodynamical 

parameters of nuclear matter. The measurement of direct photons provides a signal from the early fireball. 

Elliptic and directed flow of π 0 measured by the ECAL will be used for collision geometry and 

collective phenomena studies. 

The fast calorimeter signal could also provide an online selection of rare events, where electrons or 

photons are produced with a high transverse momentum. The physics program requires a very effective 

particle identification, high energy and spatial resolution for the reconstruction of short lived heavy 

particles (J/ψ, D, ρ mesons) via their decay products, and very fast response to operate at a maximum 

ion-ion interaction rate of about 10 MHz. 

The physics program imposes the following requirements on the ECAL: 

• large acceptance (θ < 26 ◦ ) and phase space coverage leading to a large sensitive surface of the 

detector; 

• good energy resolution of ∼ 5%/ √ E (GeV) and a few mm spatial resolution in a wide energy 

range up to 40 GeV are essential to ensure effective reconstruction of photons and π 0 s; 

• high radiation resistance. 

The large angular acceptance of the CBM experiment and the distance needed for precise time-of-flight 

detector results in a large area calorimeter system located immediately after the wall of RPC detector. 

Optimizing the cost-to-performance ratio we propose to employ the “shashlik” technology of sampling 

scintillator-lead structure readout by plastic wavelength shifting fibers. This technology has been 

successfully developed by the PHENIX collaboration at BNL [159], the HERA-B collaboration [160] 

at DESY, RD36 [161] and LHCb [162] collaborations at CERN. Recently, an extremely good energy 

resolution of ∼ 3%/ √ E (GeV) has been demonstrated for “shashlik” modules with 300 sampling layers 

[163] [164]. Adopting “shashlik” technology for CBM environment certainly requires considerable 

modifications and extensive R&D studies. 

The energy resolution of the calorimeter will directly affect the sensitivity of our experiment to prompt 

photons and π 0 s. Taking into account that the energy range of particles produced in ion-ion collisions at 

CBM is rather soft, we have to decrease the thickness of lead plates, thus minimizing the sampling term 

in the energy resolution. At the same time the very high multiplicity of about 1200 charged and neutral 

particles per heavy ion (Au+Au) central collision at 25 AGeV has to be taken into account (see also 

Fig. 6.1). Overlapping showers from neighbour tracks considerably dilute the intrinsic energy resolution 

and identification power of a calorimeter with poor transversal segmentation (or large Moliere radius). 

There are only two possibilities to minimize this effect: decreasing the Moliere radius via decreasing of 

161


Hits / cm 2 

Hits / cm 2 

10 -1 

10 -2 

10 -3 

10 -4 

10 -5 

10 -1 

10 -2 

10 -3 

10 -4 

10 -5 

10 2 

10 2 

Hadrons 

Photons 

Electrons 

Hadrons 

Photons 

Electrons 

A 

B 

X, cm 

X, cm 

Figure 6.1: The number of particles 

entering the calorimeter per cm 2 

per ion-ion collision along x axis; (A) 

for Minimum Bias events and (B) for 

central events (b < 3 fm). Electrons 

and photons have energies above 1 MeV, 

hadrons have energies above 10 MeV. 

the scintillator plates thickness or moving the calorimeter further away from the target, which certainly 

would increase the overall detector dimensions, complexity and cost. 

Discrimination between electrons (positrons) and hadrons in electromagnetic calorimeter is conveniently 

achieved by comparison of deposited energy with the measured track momentum. In CBM this method 

suffers from very high density of incoming particles. Further discrimination power could be obtained 

by using shower-shape information. Therefore we have studied the possibility to put a thin (1.5 X0) 

preshower detector in front of the calorimeter. Time information from ECAL could provide additional 

suppression of heavy hadrons. 

Longitudinal segmentation could also be useful for µ identification. The discrimination of π mesons 

is based on the fact that muon energy deposition is directly proportional to the width of calorimeter 

segments while the pion energy deposition fluctuates significantly. A possibility of µ/π separation using 

the combined information from the ECAL and a hadron catcher is being studied [165]. In this document 

we present the results of performance studies for a system consisting of preshower and electromagnetic 

calorimeter. 

6.2 Simulations 

The main aim of this chapter is to demonstrate the e/π separation achievable with the CBM calorimeter 

system. Feasibility studies of the photon reconstruction and muon identification are in progress. 

The densities of incoming particles at the front face of the calorimeter system are shown in Fig. 6.1 as a 

function of the distance from the beam pipe. The hit density varies over the calorimeter surface by two


ECAL 

region Inner Middle Outer 

Cell size 3x3 cm 2 6x6 cm 2 12x12 cm 2 

No. of channels 11712 6448 5592 

Dynamic range 0 - 40 GeV 0 - 30 GeV 0 - 20 GeV 

Average e energy (from J/ψ) 11 GeV 8 GeV 5 GeV 

ADC 12 bits 12 bits 12 bits 

Table 6.1: Parameters of the CBM electromagnetic calorimeter. 

orders of magnitude. To minimize the number of readout channels, the calorimeter should have a variable 

granularity. It is composed of inner, middle and outer regions with transverse segmentation adjusted 

to ensure an average 10 % occupancy per calorimeter cell for central Au-Au collisions at 25 AGeV. The 

overall layout of the ECAL regions is shown in Fig. 6.2. In the outer region a calorimeter module is 

read out by a single photo-detector. Modules in the middle region are divided in 4 light isolated cells 

(6x6 cm 2 ). In the innermost region modules are divided in 16 light isolated cells (3x3 cm 2 ) which corresponds 

to the 3 cm Moliere radius in the lead-plastic structure with 1:1 volume ratio. The calorimeter 

covers 12 × 9.6m 2 area and weights about 200 tons. Table 6.1 summarizes the parameters of the three 

calorimeter regions. 

400 

300 

200 

100 

0 

-100 

-200 

-300 

-400 

-600 -400 -200 0 200 400 600 

Figure 6.2: Lateral segmentation of the CBM ECAL (Numbers in cm). One block stands for 12x12 cm 2 module. 

The overall number of channels is 23752. 

The working conditions of the CBM calorimeter are illustrated in Fig. 6.3. The 2D maps of all three 

ECAL sections show the probability to find a fired cell (i.e. cell with energy deposition above the certain 

threshold) per one central Au-Au collision. Assuming independent energy depositions in neighbour cells, 

thresholds are chosen to insure less than 5%/ √ E (GeV)/ √ #cells contribution to the average energy of 

electrons (positrons) from J/ψ decays (50 MeV for outer section, 60 MeV for middle section and 80 MeV


row number 

row number 

row number 

80 

60 

40 

20 

0 

100 

75 

50 

25 

0 

100 

75 

50 

25 

0 

Central Au-Au collisions at 25 AGeV 

0 20 40 60 80 100 

column number 

Outer section 

0 20 40 60 80 100 

column number 

Middle section 

0 20 40 60 80 100 120 

column number 

Inner section 

0.5 

0.45 

0.4 

0.35 

0.3 

0.25 

0.2 

0.15 

0.1 

0.05 

0 

0.5 

0.45 

0.4 

0.35 

0.3 

0.25 

0.2 

0.15 

0.1 

0.05 

0 

0.5 

0.45 

0.4 

0.35 

0.3 

0.25 

0.2 

0.15 

0.1 

0.05 

0 

Figure 6.3: Probability to find a fired cell in all ECAL sections for central Au-Au collisions.


for inner section). In spite of the relatively large number of channels, the occupancies in the innermost 

region of all calorimeter sections are rather high. 

6.2.1 Acceptance 

The acceptance of the ECAL for detection of two particles, J/ψ → e + e − and ω(782) → π 0 γ, as a function 

of transverse momentum pT and rapidity y is shown in Fig.6.4. The mesons J/ψ and ω(782) are assumed 

, GeV/c 

T 

p 

5 

4.5 

4 

3.5 

3 

2.5 

2 

1.5 

1 

0.5 

0 

0 1 2 3 4 5 6 

y 

1 

0.9 

0.8 

0.7 

0.6 

0.5 

0.4 

0.3 

0.2 

0.1 

0 

, GeV/c 

T 

p 

0 

0 1 2 3 4 5 6 

y 

Figure 6.4: Geometrical acceptance for J/ψ → e + e − (left) and ω(782) → π 0 γ (right) as a function of pT and y. 

as detected if all their decay products hit the ECAL surface. 

The ECAL geometry can be optimized for a particular reaction. Acceptance of the ECAL for J/ψ → e + e − 

decay as a function of the CBM calorimeter area is presented in Fig. 6.5. The ECAL has a rectangular 

shape with the X dimension 20 % larger than the Y dimension to account for a bending of charged particles 

in the vertical magnetic field. Following the J/ψ reconstruction procedure, the transverse momentum 

of electrons (positrons) is required to be higher than 1 GeV/c. 

6.2.2 e/π separation 

Our studies on e/π separation are based on the attempt to use only the information of the electromagnetic 

calorimeter. The method used in the simulation is the following: 

• Use the track information to predict the impact point of a particle into the calorimeter. Construct 

the 3×3 cluster of the calorimeter cells closest to the found impact point, checking that the central 

cell has a maximal energy deposition. 

• Choose the 2 × 2 matrix with the maximal energy within the found cluster. 

• Require the energy of the maximal 2 × 2 matrix to be larger than a certain fraction of track momentum. 

Tune this cut to ensure the same electron identification efficiency (95 %) for different 

momentum intervals. 

• Check the cluster shape, requiring the contribution of the maximal 2 × 2 matrix to the total cluster 

energy to be greater than a certain fraction. Tune this cut to ensure the same electron identification 

efficiency (90 %) for different momentum intervals. 

5 

4.5 

4 

3.5 

3 

2.5 

2 

1.5 

1 

0.5 

1 

0.9 

0.8 

0.7 

0.6 

0.5 

0.4 

0.3 

0.2 

0.1 

0


acceptance 

0.7 

0.6 

0.5 

0.4 

0.3 

0.2 

Acceptance of the CBM ECAL 

40 50 60 70 80 90 100 110 120 

ECAL area, m 2 

Figure 6.5: Geometrical acceptance 

for J/ψ → e + e − as a function of ECAL 

area. 

Fig. 6.6 shows the ratio of ECAL energy in a 2 × 2 matrix to track momentum for 4 GeV/c electrons 

and pions entering the middle section of the calorimeter. The tail above unity is explained by energy 

contributions leaking in from neighbor tracks. This is the main factor limiting the e/π separation power 

of the CBM ECAL. 

Table 6.2 summarizes the results on e/π discrimination in the three sections of the CBM calorimeter 

for central Au-Au collisions. The π meson suppression, which is the ratio of accepted pions to pions 

misidentified as electrons, is shown for different momentum intervals for central events and single particles. 

The very high particle density decreases the identification power of the calorimeter, in particular 

for low momentum particles. 

ECAL 


65 (< 8 GeV) 27 (< 5 GeV) 13 (< 5 GeV) 

Central 141 (8-13 GeV) 69 (5-8 GeV) 35 (> 5 GeV) 

events 153 (>13 GeV) 250 (> 8 GeV) 

single >250 (3-8 GeV) >200 (3-5 GeV) 150 (< 5 GeV) 

particles >500 (>8 GeV) >300 (>5 GeV) >300 (> 5 GeV) 

Table 6.2: π meson suppression for three regions of the CBM ECAL obtained using ECAL information only. 

The corresponding energy range is given in brackets.


arbitrary units 


120 

100 

80 

60 

40 

20 

0 

0 0.2 0.4 0.6 0.8 1 1.2 1.4 

electrons 

E calorimeter / P tracker 

500 

400 

300 

200 

100 

0 

0 0.2 0.4 0.6 0.8 1 1.2 1.4 

pions 

E calorimeter / P tracker 

Figure 6.6: Ratio of ECAL cluster 

energy to track momentum for 4 GeV/c 

electrons (upper panel) and π mesons 

(lower panel). 

Calorimeter clusters consisting of 9 cells with almost 50 % probability have energy contributions from 

more than one particle (for central events). A preshower detector, located in front of the electromagnetic 

calorimeter, could significantly improve the suppression of π mesons. We studied a preshower detector 

consisting of a 1.5 X0 lead absorber and 10 mm thick plastic tiles with a transverse granularity following 

the one chosen for ECAL. Typical signals observed in the preshower detector for electrons and pions in 

central Au-Au collisions are shown in Fig. 6.7. Most of the electrons are arriving at the entrance of the 

calorimeter system together with radiative photons emitted in the material of TRD and RPC. Interacting 

in the preshower absorber, these particles produce large (10-15 MIP) signal in plastic tiles. Pions are 

mostly registered as MIP particles. The preshower cell is chosen by propagating the reconstructed track 

to the calorimeter system entrance. The preshower signal is required to be larger than a certain threshold 

which depends on the track momentum to guarantee the same electron identification efficiency (85 %) 

for different momentum intervals. Table 6.3 summarizes our final results on e/π discrimination in three 

sections of the CBM calorimeter system for central Au-Au collisions. 

ECAL 


117 (< 8 GeV) 48 (< 5 GeV) 41 (< 5 GeV) 

Central 311 ( 8-13 GeV) 203 (5-8 GeV) 163 (> 5 GeV) 

events 600 (>13 GeV) 580 (> 8 GeV) 

Table 6.3: π meson suppression for three regions of the CBM calorimeter system using combined ECAL and 

preshower information. The corresponding energy range is given in brackets.




300 

250 

200 

150 

100 

50 

4500 

4000 

3500 

3000 

2500 

2000 

1500 

1000 

0 

0 5 10 15 20 25 30 35 40 45 50 

electron signal in preshower 

MIPs 

500 

0 

0 5 10 15 20 25 30 35 40 45 50 

pion signal in preshower 

MIPs 

6.3 Technology 

Figure 6.7: Preshower signals for 

electrons and pions. 

The design proposed for the CBM ECAL is based on the technology developed for other electromagnetic 

calorimeters built in 1986–2004 for the experiments PHENIX, HERA-B, LHCb and KOPIO. The 

calorimeter will be of the sampling type, i.e. will be made of layers of metallic and scintillator tiles. The 

scintillator is made of polystyrene doped by 1.5% pTP and 0.04% POPOP. Typical physical properties 

of the scintillator of 1.5 mm thickness produced in the Institute for High Energy Physics [164, 166] are 

shown in Table 6.4. 

Light output Attenuation length Light yield of MIP, Main decay λmax 

(%) Anthracene cm p.e. per tile time (ns) (nm) 

54 ± 6 6.8 ± 0.5 7.1 ± 0.3 2 420 

Table 6.4: Typical physical properties of the scintillator for ECAL modules. 

The requirement to have a smaller Moliere radius with the same energy resolution leads to the need of 

thinner scintillator tiles. Available facilities at IHEP and Vladimir allow to produce tiles with variable 

thickness (see examples in Fig. 6.8 and Fig. 6.9) from 10 mm to 0.5 mm. A test “shashlik” module has 

been constructed from 280 alternative layers of 0.5 mm thick scintillator and 0.5 mm thick lead tiles. This 

module has been extensively studied at a 100 GeV/c muon beam in order to measure the light yield and 

uniformity of light collection. The light yield was measured to be ∼ 5000 photons per GeV. The maximal 

variation of response as a function of entry point coordinate is better than 40 % as shown in Fig. 6.10. 

This variation is caused by fiber to fiber nonuniformity of light collection efficiency. A substancial

6.3. Technology 169 

improvement is expected for showering electrons and inclined tracks. Nevertheless we plan a dedicated 

R&D to further improve uniformity of response for very thin plastic plates. 

Figure 6.8: Sample of scintillator tiles produced in 

IHEP. 

Figure 6.9: Set of different plastic and lead tiles produced 

in Vladimir. 

Scintillator tiles are produced with injected mold technology. The machine for the automatic processing 

using this technology installed in IHEP is shown in Fig. 6.11. A smaller Moliere radius can also be 

achieved by using an absorber with a smaller radiation length. As an alternative to conventionally used 

lead, tungsten and uranium absorbers can be considered for the CBM calorimeter. These materials have 

1.7-times better radiation length than lead. Tungsten is much harder in mechanical processing than lead 

or uranium, and research is needed to develop a relatively cheap technology to treat it. 

ADC counts 

140 

130 

120 

110 

100 

90 

left border 

-25 -20 -15 -10 -5 0 5 10 15 20 25 

right border 

local X(mm) 

Figure 6.10: Energy response of the 

test module for 100 GeV/c muons as a 

function of the impact point position 

along a 1 mm slice passing through the 

row of fibers.


6.4 Detector construction 

Figure 6.11: Injected mold machine 

for the automatic processing of the scintillator 

tiles. 

The geometry of the calorimeter system implemented into the Monte Carlo simulation package (see 

section 6.2), represents the wall of the calorimeter modules. As an example, Fig. 6.12 shows the LHCb 

electromagnetic calorimeter. The advantage of this geometry is an inherent simplicity of construction 

and good homogeneity of the sensitive surface which results in the minimum of dead zones between the 

calorimeter modules. The wall consists of three zones with different transverse cell size as described in 

section 6.2. 

Possible modifications of the above described solution, including the choice of the module granularity, 

dimensions, number of ECAL zones as well as a use of a quasi-projective geometry for the sub-parts of 

ECAL wall, is subject of further optimization. 

The preshower detector could be either constructed as a separate wall, with a granularity corresponding 

to the ECAL, or integrated into the ECAL module. The latter option is our baseline solution. The 

whole calorimeter system can be put onto a separate movable platform to ease installation and access for 

maintenance. 

6.4.1 Module design 

Each calorimeter module combines a preshower and ECAL parts, readout from the forward and rear sides 

respectively, all physically integrated into a single module box. The preshower elements are 8.4 mm thick 

lead (1.5 X0) and scintillator pads. A groove in the scintillator pad houses the helical WLS fiber which 

collect the light. The light from both WLS fiber ends is sent to SiPM located at the front surface of the 

calorimeter module. A light yield of about 20 p.h.e. in response to minimum ionizing particles has been 

demonstrated with a 5 mm thick scintillator pad [167]. 

The ECAL part of the module has a periodical structure along z-axis of metal absorber and scintillator 

tiles. Absorbers can be made of lead or other heavy metals (tungsten, uranium), and the actual choice 

of the absorber will depend on the physics requirements. Scintillator tiles are wrapped by 60µm-thick 

tyvek from both sides for better diffusive light reflection. Scintillating light is collected by optic fibers 

KURARAY KY11(200)M-DC doped by the wave-length shifter (WLS). This type of the fibers has one 

of the best light attenuation length and elasticity. The fibers penetrate all the layers with the surface 

density about 0.9 fibers/cm 2 which provides a good homogeneity of the light collection. WLS-fibers are

6.4. Detector construction 171 

Figure 6.12: LHCb electromagnetic 

calorimeter 3D-view 

from behind of ECAL towards 

the interaction region. One of 

two ECAL platforms is partially 

moved out. 

looped at the front of the cell and the ends of all the fibers are collected into one bundle at the end of 

the cell to guide the light to a photo-detector. The tower of lead-tyvek-scintillator layers is compressed 

together and held by steel strings in four corners of the cell. As an example, the LHCb modules of the 

inner, middle and outer sections, are shown in Fig. 6.13 

The actual transverse size of the module, thickness and number of sampling layers are defined by the 

physical requirements, namely by the desired Moliere radius, total effective radiation and nuclear absorption 

lengths and the energy resolution. These values depend on the sampling and can be expressed in 

terms of the relative length of the lead layers with respect to the total cell length w = hPb/htot as demonstrated 

in Fig. 6.14. Markers on the curves show some particular sampling cases with different lead and 

scintillator thicknesses which are available now in the IHEP scintillator facility [166] and in the Russian 

heavy metal industry. 

As an example, modules with a Moliere radius of 4 cm and a good energy resolution could consist of the 

lead and scintillator layers of the thicknesses 0.275 and 0.8 mm respectively. The total radiation length of 

20X0 could be realized with 390 layers and a total cell length of 466 mm. Modules with a Moliere radius 

of 6 cm could be assembled from the lead layers of 0.275 mm and scintillator layers of 1.5 mm. The total 

number of sampling layers required to provide the same radiation length of 20X0 is 360 resulting in the 

cell length of 680 mm.


, mm 

0 

X 

70 

60 

50 

40 

30 

20 

10 

h =0.275 mm, h =1.5 mm 

Pb 

Sci 

h =0.275 mm, h =0.8 mm 

Pb 

Sci 

h =0.350 mm, h =0.8 mm 

Pb 

Sci 

0 

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 

w=h /h Pb tot 

, mm 

I 

λ 

800 

700 

600 

500 

400 

300 

200 

Figure 6.13: LHCb modules. 

h =0.275 mm, h =1.5 mm 

Pb 

Sci 

h =0.275 mm, h =0.8 mm 

Pb 

Sci 

h =0.350 mm, h =0.8 mm 

Pb 

Sci 

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 

w=h /h Pb tot 

, mm 

M 

R 

90 

80 

70 

60 

50 

40 

30 

20 

10 

h =0.275 mm, h =1.5 mm 

Pb 

Sci 

h =0.275 mm, h =0.8 mm 

Pb 

Sci 

h =0.350 mm, h =0.8 mm 

Pb 

Sci 

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 

w=h /(h ) Pm tot 

Figure 6.14: Effective radiation length, nuclear absorption length and effective Moliere radius of the calorimeter 

vs the length fraction of lead w = hPb/htot. 

6.5 Photon detectors 

Custom photomultipliers have demonstrated perfect performance with “shashlik” calorimeters during the 

last 15 years. However, their relatively high price seriously limits the granularity of the system, which is 

particularly important for CBM. Therefore it would be very interesting to consider the new technologies 

(APDs, multianode PMTs, SiPMs) especially for the preshower and hadron catcher where requirements 

for energy resolution, dynamic range and linearity are not very stringent.

6.6. Readout electronics 173 

To provide high accuracy of the energy measurement and long-term stability in the high-frequency load 

environment, the photon detectors should have a low rate effect and a linear amplification in the whole 

energy range. PMTs with the required low rate effect are characterized by a stability of better than 1 % 

at a current of 20µA. The linearity should be better than 1 % in the range of pulse currents up to 50 mA. 

6.6 Readout electronics 

The electromagnetic calorimeter of the CBM experiment must handle in its hottest regions a counting 

rate compatible with the expected beam average rate of 10 MHz for A-A collisions and up to several 

100 MHz for p-p and p-A collisions. In this kind of experimental situation we need to implement a fast 

shaping of the PMT signals and a processing capable of signal deconvolution in case of events pile-up. 

The PMT signal processing could be essentially based on a Gaussian-like continuous shaping of the type 

(CR−RC) n (where for the following description n=4 should be a reasonable starting point). The shaping 

time constant is a compromise between noise reduction optimization, pile-up extent, and ADC sampling 

frequency. The latter requirement is because the cost per channel will depend in relevant extent on the 

number of Mega Samples chosen for the 12 bit ADC. Nowadays 100 MSPS 12 bit ADC can be found for 

∼20 $ and we will base our initial design on these characteristics. 

As a consequence, a (CR − RC) 4 shaping on the photomultiplier signals with 50 ns time constant looks a 

reasonable starting point for the design of the ECAL FEE, since typically 5 samples per hit at 100 MSPS 

should be sufficient, once properly processed, to reconstruct the signal amplitude after a proper baseline 

subtraction, also in the case some pile-up occurred. The digitally converted 12 bit information should 

then been fed into a processing unit that should perform the following main operations: 

• provide a self trigger based either on the single readout channel information or, better, by a clustering 

algorithm on neighbouring channels; 

• provide possibly a data size reduction by means of zero suppression; 

• append to each sampled amplitude its time stamp and spatial coordinate. 

These processing units could be implemented in FPGAs (containing DSP units) each one handling a 

group of calorimeter channels. At the actual status of the system design it is very difficult to go deeper in 

the details of the logic to be implemented in the FPGA and therefore is hard to define how many readout 

channels a FPGA will serve. Nevertheless the flexibility and the performances of the already existing 

FPGA devices (see, for instance, the ALTERA stratix II series) make us confident that a good level of 

channel integration could be achieved also with the implementation of complex logic and processing 

schemes keeping the cost per channels still reasonably low. 

6.7 Radiation hardness of detectors and electronics 

The radiation doses are obtained by summing energy depositions calculated with the CBM GEANT 

based simulation program. The expected annual radiation dose at the shower maximum for the CBM 

calorimeter modules closest to the beam pipe is 0.2 Mrad, assuming 10 7 ion-ion collisions per second 

and 5 × 10 6 seconds per nominal year. As it can be seen from Fig. 6.15, the dose is a steep function of 

the distance from the beam pipe, and varies over the inner calorimeter region by an order of magnitude. 

The middle and outer section modules do not suffer from radiation. 

The radiation can affect the quality of the optical components made of plastic materials, namely the 

scintillating tiles and the WLS fibers. Both, light yield and transmission through the tiles and fibers may 

decrease with increasing dose. The detailed studies performed for the LHCb electromagnetic calorimeter


[162] has shown the modest degradation of constant term in energy resolution of “shashlik” modules after 

5 Mrad accumulated dose. For the CBM calorimeter such a degradation is much less important. 

Dose, rad/year 

10 5 

60 80 100 120 140 160 180 200 

distance from the ECAL center, cm 

6.8 Working packages, timelines, costs 

Figure 6.15: The expected annual radiation 

dose distribution along the xaxis 

at y=0 

Activity Time line 

Simulation package development, optimization of calorimeter geometry and module 

parameters, reconstruction and particle identification algorithms implementation 

2004–2006 

Manufacturing and characterization of pilot samples of thin scintillator (0.5–1.0 

mm) 

2004-2006 

Production of prototypes with different sampling and transverse cell sizes and their 

tests at IHEP, ITEP and GSI beams 

2005–2006 

Construction of a large-scale matrix of pre-production modules and studies of the 

energy and spatial resolutions, and PID issues at the beams 

2006–2008 

R&D of monitoring system 2006-2007 

R&D on photon detector: tests with the prototype of different PMT, SiPM and APD 2005–2007 

Module mechanical design 2006–2007 

Calorimeter mechanical structure design 2007–2008 

Definition of the FEE project and design 2004–2006 

R&D on the shaping filter 2007–2008 

Implementation of the readout board logic 2007–2008 

Prototype and tests 2008–2009 

FEE mass production 2010–2011 

FEE installation and commissioning 2011–2012 

Mass production of the modules 2008–2012 

Calorimeter assembling and commissioning in GSI 2011–2012

6.8. Working packages, timelines, costs 175 

The cost estimate is extrapolated from the known costs of other calorimeters of the similar type and 

shown in Table 6.5 

Element price per unit, quantity cost, M 

Inner modules 2000 732 1.5 

Middle modules 1000 1612 1.6 

Outer modules 600 5592 3.4 

Mechanical structure 0.2 1 0.2 

PMT 90 23752 2.2 

SiPM 10 23752 0.2 

High voltage system 50 23752 1.2 

FEE ECAL channel 50 23752 1.2 

FEE PRS channel 30 23752 0.7 

Total 12.2 

Table 6.5: Cost estimate for ECAL.

176 Electromagnetic Calorimeter (ECAL)

7 Superconducting dipole magnet 

The requirements for the CBM dipole magnet are: aperture of 1 x 1 m 2 , a bending power of about 1 Tm, 

and negligible stray field within the volume of the RICH radiator. Moreover, the magnetic field is needed 

to deflect the δ-electrons being produced abundantly in the target by the intense heavy-ion beam. 

We have designed two versions of dipole magnets which differ in their field configuration. Track reconstruction 

simulations are under way to decide upon the two magnetic field versions. 

7.1 Dipole with parallel pole shoes 

The first magnet concept is shown in figure 7.1. The magnetic field is calculated using the TOSCA code 

and has been implemented in the GEANT simulation code. Figure 7.2 shows the y component of the 

field along the beam axis at x = 0 and various vertical distances from the midplane. A possible technical 

realization of the magnet is illustrated in figure 7.3. 

Figure 7.1: Model of the dipole magnet used for TOSCA calculations 

177


Figure 7.2: Magnetic field component in vertical direction as calculated with TOSCA 

Figure 7.3: Possible realization of the superconducting dipole magnet 

7.2 Dipole with inclined pole shoes 

The second magnet version is illustrated in figure 7.4. Details of the construction of the iron yoke, the 

pole shoes and the coils are presented in figure 7.6. 

The yoke of the magnet consists of the top and bottom girders, the side struts and the poles. The magnet 

pole has a trapezoidal shape with the corners rounded with the radius 61 mm. The poles are fastened 

on the girders and make angles 30 ◦ with the median plane. The top and bottom girders consist of three 

separate parts each. The racks of the magnet have the shape of a trapezoid. The weight of any structural 

element does not exceed 10 tons. Therefore, the crane available in the cave will be sufficient for the 

assembly of the magnet. All parts of the magnet yoke are manufactured of steel with low carbon content 

(Steel 10). The girders and racks are assembled in one structure and are fastened with the help of bolted

7.2. Dipole with inclined pole shoes 179 

Figure 7.4: Model of the dipole magnet with inclined pole shoes as used for TOSCA calculations 

Figure 7.5: Field configuration of magnet with inclined pole shoes 

connections. The structural elements are fastened together with the help of pins and inserts. The magnet 

poles are fastened on the top and bottom girders with the help of special bolted connections. The field 

inside the gap of the magnet is formed by the poles of a trapezoidal shape. The cryostats of the winding 

are fastened on the poles. The load from ponderomotive forces originating in the winding excited by


Figure 7.6: Construction of the dipole magnet with inclined pole shoes 

electric current is transmitted to the poles. The excitation winding is made from a superconducting cable 

with the cross-section 7*4.5 mm. The wires of the cable are manufactured of superconducting Ni-Ti 

threads (niobium and titanium alloy). The superconducting threads are in a copper matrix and are placed 

in a common aluminum shell. The ratio of the superconductor cross section area to the cross section 

area of copper is 1/3. The ratio of the SC cross section area to the aluminum shell cross section area 

is 1/12. The cable is insulated by two layers of a 20-micron thick polyamide film and two layers of a 

fiberglass ribbon of the thickness 0.1 mm and width 20 mm with half-width overlapping. While winding 

the excitation coil slots 10 mm wide and 0.2 mm deep occur between adjacent layers of the winding. 

The slots provide penetration of liquid helium to the cable and improve heat dissipation. The winding 

consists of two coils (a pancake-like design), each of them in a separate helium vessel of the cryostat. 

The cryostat of the excitation winding consists of a helium vessel, a nitrogen shield and a vacuum shell. 

The nitrogen shield and the vacuum shell transmit load from ponderomotive forces originating in the 

winding to the magnet pole. The windings are insulated from the walls of the helium vessel by frame 

insulation of 2 mm thick fiberglass with vertical grooves 1 mm deep and 7.5 mm wide. The grooves serve 

as channels for liquid and gaseous helium flow. The welded helium vessel serves as a helium reservoir. 

The external surface of the vessel is polished. The radiation heat flux from the nitrogen shield to the 

helium vessel is 0.25 W. The helium vessel is fastened to the nitrogen shield by supports with maximum 

heat resistance. Heat flux to the helium vessel due to heat conduction of the supports does not exceed 

2.0 W. The nitrogen shield is manufactured of copper. Copper tubes for pumping of liquid nitrogen are 

soldered to the shield. The nitrogen shield is hanged in the vacuum shell with the help of special supports. 

The supports are fabricated of a material with maximum heat resistance. The walls of the nitrogen shield 

are polished and covered with five layers of vacuum insulation. It takes away approximately 40% of 

the radiation heat flux to the nitrogen shield from the vacuum shell. The vacuum shell is manufactured 

of stainless steel. The covers and hatches in the shell provide access inside for mounting equipment. 

The internal wall of the shell leans on the magnet pole and is fastened to it. The selected thickness of 

the vacuum shell walls is 4 mm. The wall thickness was selected from the conditions of stability and 

maximum permissible sagging 0.2 mm. The shell is supplied with nipples for attachment of vacuum

7.2. Dipole with inclined pole shoes 181 

Figure 7.7: Construction of the superconducting coils 

pumps and pressure sensors. The internal surface of the vacuum shell is polished. The top and bottom 

cryostats are connected with each other by two cryo-vacuum subs. The joints of all communication lines 

are placed inside the subs. They are the cryostat monitoring systems, helium and nitrogen joints, current 

joints of the top and bottom coil windings. The upper cryostat is supplied with a filler neck. Two devices 

for current input, 4 leads each, liquid helium and liquid nitrogen inputs, control and sealing devices, 

indicators of operating liquid levels in the cryostat, outputs of sensors of transition to the normal phase 

and temperature sensors, etc. are placed on the neck. The pipelines and the communication joints in the 

filler neck yield heat flux to the helium vessel 3.75 W. The main specifications of the magnet are given 

in Table 7.1. 

Parameter Quantity Dimension 

Weight of yoke 37 ton 

Coil windings 270 

Distance between poles maximal 845 mm 

Distance between poles minimal 368 mm 

Ampere turns 600000 

Field length 1200 mm 

Maximum field 1.7 T 

Time to cool down 48 hours 

Time to ramp-up field 45 minutes 

Thermal stream to Helium vessel 6 W 

Thermal stream to Nitrogen screen 30 W 

Table 7.1: Main specifications of the magnet

182 Superconducting dipole magnet

8 Diamond start detector 


CVD Diamond Detectors (CVD-DD) are radiation-hard and fast particle sensors suitable for operation 

in primary Heavy-Ion (HI) beams of beam intensities up to 10 9 ions/s [168]. We intent to apply CVD-D 

micro-strip detector(s) of a thickness 100 µm ≤ dD ≤ 300µm as START (T0) detector(s) for the ToF 

measurements. The detectors will be optimized with respect to an intrinsic time resolution in the order of 

50 ps and a count rate capability of ≥ 5 · 10 6 ions/s per strip. Presently, a detector of a strip and readout 

pitch of 50 µm at a strip width of 25 µm is discussed. 

Polycrystalline CVD Diamond (PC-CVD-D) strip detectors of such a layout (Fig. 8.1)) have been reliably 

produced and extensively tested in the past from the RD42 Collaboration striving for minimum 

ionizing particles (MIP) tracking near the interaction region in high-energy experiments at high luminosity 

colliders [169]. Using muon beams and VA2 electronics, a spatial resolution of 10 µm to 20 µm and 

a hit efficiency of 86.7% have been achieved. 

Figure 8.1: A CVD Diamond Beam Telescope 

(The RD42 Collaboration). Two hybrids 

carrying PC-CVD diamond strip detectors 

and readout chips. The 2×2 cm 2 big sensors 

of a strip- and readout pitch of 50 µm are 

centered over a hole in the aluminum frame. 

However, depending on diamond quality and thickness (section 8.4) the charge collection efficiency 

(CCE) of PC-CVD-DD is reduced to 20% - 50%. The main charge loss is due to the grain boundaries 

within the polycrystalline texture of the material. Since operation with heavy projectiles and light ions 

down to protons is foreseen for CBM Single-Crystal (SC) CVD-DD of almost 100% CCE [170] must 

be considered as well. In section 8.2 relevant data obtained so far from dot- respectively pad-detectors 

of these two types of diamond is presented. The suitability of the above described layout is discussed 

in section 8.3, 8.4 and 8.5 with respect to the initial beam parameters published in reference [171]. The 

parameters have been scaled for 197 Au ions and protons of lowest and highest kinetic energy. Special 

care is spent to consequences of the small beam focus size required in CBM, which affects both, the 

specifications of FE electronics (section 8.5) and the choice of the diamond detector thickness and quality 

(section 8.4). The goal is to minimize avoidable background and time-zero jitter, maintaining a good 

Signal-to-Noise (S/N) ratio at an acceptable ion rate per detector channel. 

183


8.2 Properties of CVD diamond detectors 

8.2.1 Pulse shape characteristics 

The high mobility of electrons (2400 cm 2 V −1 s −1 ) and holes (1600 cm 2 V −1 s −1 ) in diamond predicts 

the suitability of DD for timing applications. GSI has a long experience with the operation of fast HI 

diamond detectors placed in the primary beam and with development of suitable single-channel Diamond 

Broadband Amplifiers (DBA [172], 2 GHz) and low threshold discriminators (LTD [173]). 

The left plots in figures 8.2 and 8.3 show 241 Am-α-signals of Eα = 5.5 MeV, which are obtained from PC 

(Fig. 8.2) respectively SC (Fig.8.3) material using such current amplifiers. The signals are recorded with 

fast Digital Storage Oscilloscopes of a bandwidth of 1.5 GHz and a single-shot resolution of 10 GS/s. 

Due to the short range of 12 µm of α-particles in diamond the drift of electrons and holes can be studied 

separately. The measurement of the uniform and fast rise time of the signals (trise

8.2. Properties of CVD diamond detectors 185 

Figure 8.3: Hole drift in SC-CVDD (The NoRHDia Collaboration) [174]. Signals from an 241 Am source measured 

with a single-crystal diamond dot detector of 300 µm thickness and a capacitance of 0.5 pF. 

time distribution shown on the right plot of the figure is given by the detector thickness whereas the 

narrow width of it predicts both a good time and energy resolution. 

Obviously, this type of detector in conjunction with DBA-type FEE operates as a fast diamond drift 

chamber of 100% CCE and an expected count-rate capability at operation field of about 10 8 ions/s per 

detector channel. 

8.2.2 Intrinsic time resolution of HI diamond detectors at SIS energies 

Using various heavy projectiles the intrinsic time resolution of PC-CVD-DD has been measured frequently 

to be well below 50 ps [168]. Recently, in a FOPI beam test with 181 Ta ions of 1 AGeV a 

σintr = 22 ps has been achieved using 500 µm thick samples and the DBA based RPC FEE of FOPI (Figure 

8.4). A similar value (σintr = 29 ps) has been obtained earlier by the HADES Collaboration using 

52 Cr ions of 650 AMeV and two diamond detectors of a thickness of 100 µm. 

Based on the observation of fast and uniform rise times of SC-DD signals (Figure 8.3) and of a better 

S/N ratio we expect an intrinsic time resolution for this type of DD at least as good as in the PC case. 

8.2.3 MIP timing with PC diamond detectors 

First encouraging results [175] have been obtained using 90 Sr electrons (E β max = 2.4 MeV) and two diamond 

samples of a thickness of 500 µm and of a CCE of 42%. The PC-DDs were mounted in a stack 

in front of a plastic scintillator used as trigger detector. Figure 8.5 shows on the left plot (arb. units) the 

) signal after walk correction and on the right plot 

pulse-height dependence of the time difference (Tlin D 

the Tlin D distribution for amplitudes in the indicated range of 750 < ET < 1000. Fitting the spectrum with 

a Gaussian a time resolution of σintr = 95 ps is estimated assuming equal contribution of both detectors 

to the width. 

However, due to a mean value of the collected charge per 90 Sr electron of 7560 e − corresponding to an 

amplifier noise signal of 13000 e − the detection efficiency is only about 3%. New generation diamond 

timing amplifiers are needed for this application. A CMOS based charge-sensitive but fast amplifier


Figure 8.4: Preliminary data obtained with 181 Ta ions of 1 AGeV (The FOPI Collaboration). The time spectrum 

measured with two PC diamond detectors of a thickness of 500 µm mounted perpendicular to the primary beam 

(left) is compared to the time spectrum measured with one of the diamonds against the time given by the currently 

used FOPI start detector (plastic scintillator detector). 

Figure 8.5: The walk corrected time difference is plotted versus the pulse height of one of the diamond samples 

(left). The one dimensional time spectrum (right) fitted with a Gaussian shows an intrinsic resolution of σintr = 

95 ps for an amplitude range 750 < ET < 1000. The tails of the distribution are excluded in the fit. 

prototype (rise time ≈ 1 ns) is currently under development at GSI (The NoRHDia Collaboration). It will 

be used in conjunction with a fast filter amplifier (5 - 10 ns Gaussian shaping time) allowing to improve 

significantly the S/N ratio. The experience from these developments will be exploited for diamond strip 

detector FEE. First reliable results are expected in spring 2005. 

8.2.4 Suppression of trigger rate and background using START-VETO devices 

The HADES experiment is designed for beam rates of up to 10 8 HI/spill. Due to the short intrinsic dead 

time (1 -2 ns) of diamond detectors the required beam intensity can be accepted. Two identical diamond 

strip detectors (START-VETO device) of 100 µm thickness located 75 cm upstream and downstream of

8.2. Properties of CVD diamond detectors 187 

the target are used to generate a start signal for the time-of-flight measurements. The start signal rate is 

reduced by two orders of magnitude by rejecting beam particles not reacting in the target. 

In order to distinguish and suppress pions with almost light velocity from leptons a good time-of-flight 

resolution is required. In Figure 8.6 ToF spectra measured in a commissioning run with 52 Cr ions on 

27 Al are shown. The distance between START and ToF is 2.1 m. 

Figure 8.6: ToF spectrum of all particles detected (left). ToF spectrum of electron candidates, as selected by 

means of position correlation between RICH and ToF hits. The data was obtained without magnetic field. 

Data taken from all 384 ToF segments is included in both spectra. In the left plot the time-of-flight 

spectrum of all particles arriving within 20 ns at ToF is shown. The lepton candidates are selected by 

position correlations of hits in the RICH and the segmented plastic scintillator wall (ToF detector). The 

time-of-flight spectrum of those candidates is shown in the right plot. The intrinsic time resolution of the 

START-VETO device amounts to σintr = 29 ps. The measured time-of-flight of σ = 233 ps is defined by 

the ToF detector and is affected in this spectrum in addition by calibration uncertainties of ToF segments. 

8.2.5 Pulse-height distributions in CVD-diamond detectors 

Broad pulse-height distributions are obtained with PC-CVD-DD from all kinds of mono-energetic charged 

particles from MIP up to highly ionizing heavy ions. In opposite to this, an excellent alpha resolution 

has been measured recently with SC-DD of 300 µm thickness (NoRHDia). 

In Figure 8.7 two representative spectra are plotted. On the left side the collected charge spectrum of 

84 Kr ions of 650 AMeV measured with a 100 µm thick PC-DD is shown revealing an amplitude dynamic 

range of 3 and a pulse-height resolution ΔQ/Q ≈ 1. On the right plot the energy spectrum of a mixed 

nuclide α-source ( 239 Pu, 241 Am, 244 Cm) is presented. All satellite lines are resolved. A preliminary 

calibration gives an energy resolution ΔE of about 20 keV. 

Of course, charge-sensitive spectroscopic measurements have been performed in order to obtain such 

spectra. Nevertheless, the shape of the pulse-height distributions must be considered differently for each 

type of diamond timing detector with respect to the design of FEE electronics including discriminator 

thresholds and possibly needed time walk corrections.


Figure 8.7: Typical collected charge distributions of mono-energetic particles in CVD-D detectors. Spectrum of 

84 Kr ions of 650 AMeV measured with a 100 µm thick PC-DD (left plot); Spectrum of a mixed nuclide-α-source 

( 239 Pu, 241 Am, 244 Cm) measured with a SC-DD of 300 µm thickness (right plot). 

8.3 Discussion of the strip design with respect to the beam parameters 

Beam parameters are given in Reference [171] for 238 U 92+ ions of highest possible kinetic energy, 

i.e. 34 AGeV. Table 8.1 shows corresponding horizontal (h) and vertical (v) parameters for 197 Au 79+ 

ions and protons of different kinetic energies. The uranium values have been scaled according to the 

relations ε ∝ 1 

Bρ 

∼ 1 

βγ . 

Beam parameters 197Au, 34 AGeV 197Au, 10 AGeV p, 30 GeV p, 90 GeV 

Time structure D.C. during spill 

N (50% duty cycle) 

/ions s−1 1 · 109 Transverse 

ε/mm mrad 

emittance 0.6 (h) × 0.3 (v) 2 (h) × 1 (v) 1.7 (h) × 0.9 (v) 0.6 (h) × 0.3 (v) 

Momentum spread ±1 · 10−4 Beam spot radius /mm 0.5 (h) × 0.5 (v) 

beam angles /mrad 1.2 (h), 0.6 (v) 4 (h), 2 (v) 3.4 (h), 1.8 (v) 1.2 (h), 0.6 (v) 

Table 8.1: Beam parameters for 197 Au 79+ ions and protons of two different beam energies. 

8.3.1 Beam dimensions and beam intensity load at different distance from the CBM target 

assuming 50 µm strip and readout pitch at a strip width of 12 µm 

Figure 8.8 shows preliminary beam dynamic feasibility studies [176]. Both possible target positions are 

indicated: the HADES target at a distance of 2 m and the CBM target at a distance of 12 m downstream 

the last quadrupole. Assuming the designed beam focus of 0.5(v) × 0.5(h) mm 2 at the CBM target 

position and transverse emittance of 0.6 (h) × 0.3 (v) mm mrad, beam dimensions at different distances 

from the target are plotted in Figure 8.9. The elliptic beam spots become almost circular at a distance of 

10 cm from target. 

The beam intensity profile is presently not available. In order to estimate roughly the beam load at the 

innermost strips the following assumptions have been made:

8.3. Discussion of the strip design with respect to the beam parameters 189 

Figure 8.8: Beam dynamic calculations (feasibility studies) for the HADES-CBM beam line. 

• The requested ion rate of 10 9 /s is within the 4σ-width of a Gaussian intensity distribution. 

• The beam intensity is distributed over the total number of strips. 

• About 65% of the beam is hitting homogeneously the inner strips placed at the 2σ-width of the 

intensity distribution. 

The rates per inner detector strip are plotted in the blue plot of Figure 8.9. The numbers indicate the 

corresponding total number of detector channels at each distance away from target. 

Figure 8.9: Calculated beam dimension (full-size) in horizontal (red dots) and vertical (black dots) direction for 

different distance from the CBM target. The ion rate on the innermost strips (blue dots) has been estimated for 

65% of the beam intensity distributed on 2/4 of the strips around the beam axis. The total number of detector strips 

needed is given in blue numbers.


8.3.2 Time Zero precision 

Influence of momentum spread 

At relativistic motion the relative time jitter is proportional to the momentum spread according Δt/t = 

−γ −2 · Δp/p. Due to a Δp/p = 10 −4 the uncertainty of the time zero (ΔT0) is negligible. In Figure 8.10 

the calculated values for 197 Au ions of 34 AGeV respectively 10 AGeV as well as for protons of 30 GeV 

respectively 90 GeV are plotted versus the distance between START detector and CBM target. 

Figure 8.10: Calculated total T0 variation for different distances between START detector and CBM target. 

Influence of detector layout and strip readout 

If a larger distance from the target must be chosen in order to relax the count rates of the inner strips, 

the signal propagation time within each strip is not negligible because it leads to a position dependence 

of T0. Assuming light velocity, a 15 mm length of a strip contributes with 50 ps. One can think to 

readout the strips on both sides and to correct time with position, if possible, or to separate them into 

two independent detector channels of half the length gaining such a way in addition a factor of two lower 

rates per detector channel. 

On the other hand, a detector placed 0.5 m upstream of the CBM target can be much smaller, containing 

only 24 strips. The ion rates per inner strip increase to about 5 x 10 7 /s (Figure 8.9) and the use of a 

START-VETO device (section 8.2.4) is strongly recommended. A trigger-rate reduction of two orders of 

magnitude can be achieved. However, it could be useful to discuss single-channel amplifiers combined 

with fast logic devices able to process rates up to 10 GHz. Such devices are implemented for instance in 

fast clock distribution systems.

8.4. Influence of diamond material type and thickness 191 

8.4 Influence of diamond material type and thickness 

A radiation length of 18.8 cm is given in data tables for carbon (compare silicon: 9.4 cm). However, 

the total reaction probability of 197 Au ions in 100 µm diamond is estimated to be ∼ 0.56%. The energy 

needed to create an e-h pair in diamond is 13.4 eV, which is about 4 times higher than for silicon. A 

MIP produces only 36 e − per micrometer. Therefore, the detector thickness becomes a crucial parameter 

especially in the case of minimum ionizing protons and very light ions. 

8.4.1 Available types of CVD-D material and their corresponding detector properties 

Free-standing Electronic Grade (EG) CVD-D material, being the only available quality suitable for 

charged particle detection, is classified according to its crystal texture to PC- or SC-type diamond and 

according to its finishing and surface preparation to polished Detector Grade (DG)- or As Grown (AG) 

material. An overview of material parameters and the corresponding detector properties at operation bias 

is given in Table 8.2. Values marked with asterisk are presently not measured but expected. 

Material parameters PC-AG-CVDD PC-DG-CVDD SC-CVDD 

Radiation hardness for p, π, e and fast n (1MeV) ∼ 1015 cm−2 ∼ 1015 cm−2 ∗ 

Largest possible area [mm] ∅ 100 ∅ 100 6 × 6 mm2 Free-standing thickness [µm ] 80 - 2000 250 - 600 250 - 450 

CCE [%] 25 60 100 

MIP signal [e− ]/300 µm 2700 6480 10800 

197Au, 34 AGeV signal [e− ]/300 µm 1.7 · 107 4.0 · 107 6.7 · 107 ∗ 

Signal rise time [ps] < 500 ps < 500 ps < 500 ps 

Signal drift time [ps]/300 µm


(rise time ∼ 1 ns) low-noise amplifier for this purpose with an enhanced S/N ratio is under development 

at GSI within the frame work of the NoRHDia Collaboration. 

8.4.2 Multiple scattering and energy straggling 

The energy straggling ΔE/E is negligible with respect to the time-zero definition. For 197 Au ions of 

10 AGeV kinetic energy it amounts to 6.8·10 −5 and for 34 AGeV to 5.3·10 −5 , respectively. Although the 

proton values are slightly higher (∼ 1.8 · 10 −4 for both considered energies) the same can be concluded 

also for this case. 

Assuming detector thicknesses as proposed in section 8.4.1 the multiple scattering (σ) of 197 Au ions and 

protons in 100 µm respectively 300 µm diamond is given in Table 8.3. 

dD ΔΘx(σ) ΔΘx(σ) ΔΘx(σ) ΔΘx(σ) 

(µm) ( 197 Au, 10 AGeV) ( 197 Au, 34 AGeV) (p, 30 GeV) (p, 90 GeV) 

100 0.008 0.002 

300 0.013 0.004 

[mrad] [mrad] [mrad] [mrad] 

Table 8.3: Multiple scattering of 197 Au ions and protons in 100 µm respectively 300 µm diamond. 

The increased beam spot dimensions on CBM target produced from detectors of such thickness are plotted 

in Figure 8.11 versus possible START detector positions towards the last quadrupole. Full straggling 

has been considered in the plot. 

Figure 8.11: Resulting beam spot diameters 

at target position due to 197 Au-straggling in a 

100 µm thick PC-AG-DD, respectively proton 

straggling in a 300 µm SC-DD placed at different 

distances between CBM target and last 

quadrupole. 

This result leads possibly to a serious problem. It must be simulated how this focus enhancement affects 

the required energy density on target. 

8.5 FE Electronics 

The development of FEE for diamond micro-strip ToF detectors is challenging. The signal processing 

will require multi (16-64) channel FE chips that perform several tasks: charge detection, hit discrimination, 

time stamping, hit de-randomization and serialization.

8.6. Summary, concluding remarks and outlook 193 

Charge detection 

This task is simplified in the heavy-ion scenario thanks to the huge charge of 2 pC produced for instance 

per 197 Au ion in a 100 µm sensor. An amplifier operated in a fairly small gain will be sufficient so that a 

high bandwidth can be reached rather easily. The proton case with charges in the fC range is much more 

challenging and more power will be required in the preamplifier to achieve a sufficient speed. The worst 

case pulse repetition rate of 5 · 10 6 requires shaping times in the range of 10 ns which is still compatible 

with the planned 0.18 µm chip technology. 

Hit discrimination 

The threshold for a hit can be set individually in every channel to cope with possible variations in the signal 

amplitude. A coarse pulse height information may be required to correct for time walk, in particular 

in the proton case. A simple method is the measurement of the time over threshold by time stamping also 

the falling edge of the discriminator output signal. This solution does not require additional fast analog 

circuitry. 

Time stamping 

The continuous time stamping every 200 ns on average in the most populated strips can be performed by 

latching the state of a continuously running time stamp generator. This generator can, for instance, be 

implemented as a self-running ring oscillator locked to a reference clock. The required timing precision 

of below 50 ps probably calls for advanced circuit solutions like multiple with shifted phases or analog 

interpolation. Several solutions are presently being investigated. A significant problem at this required 

precision are cell-to-cell delay variations which must be reduced as much as possible by appropriated 

circuit techniques and calibration. 

Hit de-randomization and serialization 

The raw hit information must be decoded as soon as possible to reduce the data volume. It is queued for 

serialization to free the time stamping latches for a new hit so that no dead time is introduced. This can 

be guaranteed by appropriate FIFO-type structures. The maximum data volume for a 32 channel chip 

amounts to 32 strips × 5 · 10 6 hits/sec/strip × 16 bits/hit = 2.5 Gbit/s. This can still be handled by the 

serial links which are developed in a 0.18 µm technology as a general purpose optical interconnect for 

CBM. The serializer block will be included on the FE chip. 

8.6 Summary, concluding remarks and outlook 

Suitability of the discussed detector setup(s) and strip design 

Large area polycrystalline diamond detectors are discussed as START detectors for CBM experiments 

using heavy projectiles and single-crystal polished samples of limited area as START detectors for protons. 

In both cases a strip- and readout pitch of 50 µm at a strip width of 25 µm is considered. Assuming 

a beam intensity of 10 9 ions/s, about 256 strips are needed in order to achieve a comfortable rate of 

5 · 10 6 ions/s/ inner strip. A distance of 5 m from the target leading to a detector size of about 15 × 

9 mm 2 must be chosen in this case. For a distance of 2 m 160 strips are needed and the FE chips must


cope in this case with about 8 · 10 6 /s/ inner strip. A factor of two lower rate is gained if the strips are 

separated in two parts reducing in addition the signal propagation time within each strip. 

Considering the size of the detector(s), distances closer to the target are advantageous. However, the 

beam load on the inner strips becomes then a serious problem. The only possible solution discussed 

presently is the use of a START-VETO device reducing the trigger rate by two orders of magnitude 

as demonstrated by HADES. The VETO detector can be mounted in a place not affecting the silicon 

trackers. 

Heavy-ion START detectors 

PC-AG-CVDD of a thickness in the order of 100 µm is the best choice for this case. GSI has a long 

time successful experience with this type of material and application. Any desirable total size and shape 

is available. The START detectors can be placed in any distance from target acceptable from FEE and 

data acquisition. If a reduction of trigger rate and background is needed a VETO detector must also be 

used in this case. The price of PC-AG-CVD-D is 250 US$/cm 2 and the delivery usually 6 weeks from 

order. The estimated costs for the manufacture of the electrodes are about 5000 . In case of new mask 

production this amount can be slightly higher. 

Proton START detectors 

SC-CVD-D samples of at least 300 µm thickness must be used in this case. The expected total reaction 

probability is 0.14% and multiple scattering of the beam is not negligible. 

The SC material is rather new and not yet easily available. Our diamond suppliers [177] consider it still 

as an R&D object, however, with very promising test results. Larger samples up to 10 × 10 mm 2 are 

expected in the next 2-3 years. In a long-term, wafers as large as 1 inch in diameter are also expected. 

Presently however, since active areas larger than 5 × 5 mm 2 are needed in order to reduce the ion 

rates/channel, mosaic detector techniques and assembly prototypes must be developed. The price of 

SC-CVD-D is about 800 US$ for 3.5 × 3.5 mm 2 and the delivery about 8 - 10 weeks from order. 

FE Electronics 

Novel broadband FE chips of high sensitivity and speed are needed. It appears useful to discuss in a first 

round the same design as for the RPC detectors. Nevertheless, single-channel amplifiers for small size 

detectors must be considered as well 

Simulations 

All estimations discussed above must be simulated in realistic CBM experiment scenarios. Particularly, 

background production and beam spot widening due to START detectors must be compared to the advantage 

which can be taken by the implementation of diamond START-VETO devices.

9 Common Front-End Electronics Aspects 

This section discusses all aspects of a common front-end electronics architecture. The first two sections 

discuss the general aspects and the over all requirements as defined at this point in time, together with 

microelectronics boundary conditions and available building blocks. The resulting architecture is then 

outlined in section 9.3. The required advanced building blocks are detailed in the following sections, discussing 

analog memories, multi-purpose ADCs, clock recovery, data communication and miscellaneous 

issues. 

9.1 General Considerations, Requirements 

The CBM experiment foresees a variety of sub-detectors, including a silicon tracker, RICH, TRD, ToF 

and ECAL. Their architectures are outlined in appropriate sections. Their requirements with respect to 

front-end electronics and readout are not yet finalized. However, with respect to the front-end electronics 

three groups of detectors and appropriate electronics can be defined. 

• The silicon tracker is, unlike the other detectors, mounted close to the reaction zone and is therefore 

exposed to very high radiation levels [178, 179]. Correspondingly the electronics has to be 

radiation hard in order to survive the projected 10 years of operation in the experiment. Radiation 

hard designs differ from commercial grade implementations by the shape of the transistors and 

other design techniques, generally resulting in a four fold higher space requirement and cost per 

transistor or logic cell and an appropriate higher power and lower speed rating in the same technology 

[178,179]. Given such high penalties for radiation hardness it is not advisable to use radiation 

hard techniques where they are not required. Therefore it is expected that the electronics, mounted 

on the silicon tracker, will be developed mostly independently of the other detector systems. 

• The Time of Flight system has very high requirements with respect to the timing of the electronics 

in order to achieve the target 25 ps resolution. In order for the electronics not to significantly 

contribute to the ToF timing resolution the clock distribution and signal amplification has to operate 

with less than 10ps peak to peak jitter. The corresponding requirements of the remaining detectors 

in CBM are more than an order of magnitude relaxed. At this point in time it is not definitely 

possible to judge whether or not such requirements can be met without extra effort and cost by 

standard electronics (see also the discussion in section 9.7.1). 

• The remaining detector systems TRD, RICH and ECAL do not require radiation hard electronics 

or very high-speed electronics, like RAD in case of the ToF. For the radiation levels of the named 

detectors refer to chapter 21. Using modern deep submicron electronics, these radiation levels, 

applied over the lifetime of the experiment do not produce significant total dose effects, allowing 

the use of commercial grade design techniques and silicon libraries. Single event upsets can be 

detected and corrected by appropriate fault tolerant design techniques. The TRD requires at least 

600k channels with 1. . . 10 cm 2 size, the RICH 120k channels with 0.4 cm 2 size and the ECAL 

requires about 23k with 10 cm 2 to 140 cm 2 sized channels. The digitization requirements range 

from 10 to 12 bits at up to 50 MSPS. The quoted channel densities require the use of highly 

integrated electronics, in order to fit into the space requirements. The large channel count of the 

TRD and RICH detector also favors microelectronics for cost reasons. 

195


Figure 9.1 sketches the typical building blocks found in modern detector readout electronics. The first 

element, the preamplifier, forms the interface to the sensor or detector and amplifies the typically weak 

signals to defined internal working signal levels. It is followed by a first analogue filter, which implements 

the required anti aliasing filter to match the following time discrete stages. It may also include 

some first signal shaping, for instance converting the step function, as measured by gas detectors, into 

a pulse with proportional amplitude. These prepared analog signals, which are implemented best in a 

differential manner, are then digitized and processed further by appropriate digital filters. It is a general 

trend to digitize as early as possible and to implement the signal processing in digital filters. Digital logic 

presents a variety of advantages, given enough bits, such as error free signal processing, no additional 

introduction of electronic noise, possibility relatively good technology independence, by using standard 

cells, just to name a few. The processed analogue signals are then zero suppressed in the next processing 

stage, implementing typically multi threshold hit finder algorithms. Depending on the architecture of the 

readout and trigger system the data is stored intermediately and finally shipped off the detector, using 

some back-end drivers, which often include high-speed serializer and electrical-optical signal converters. 

Si Si Strip Strip 

Pad Pad 

GEM's 

GEM's 

PMT PMT 

APD's 

APD's 

PreAmp PreAmp 

Anti- Anti- 

Aliasing Aliasing 

Filter Filter 

pre pre 

Filter ADC 

Filter ADC 

Sample Sample rate: rate: 

10-100 10-100 MHz MHz 

Dyn. Dyn. range: range: 

8...>12 8...>12 bit bit 

digital digital 

Filter Filter 

'Shaping' 'Shaping' 

1/t 1/t Tail Tail 

cancellation cancellation 

Baseline Baseline 

restorer restorer 

Hit Hit 

Finder Finder 

Hit Hit 

parameter parameter 

estimators: estimators: 

Amplitude Amplitude 

Time Time 

Backend Backend 

& Driver Driver 

Clustering Clustering 

Buffering Buffering 

Link Link protocol protocol 

Detector specific generic 

Figure 9.1: Typical generic electronics chain 

The implementation of digital filters and digital hit selection has the draw-back that the ADCs have to 

be operating at constant walk, with the consequence of the ADC digitizing baseline signals most of its 

operation time, depending on the occupancy of the detector. 

The building blocks shown Fig. 9.1 are lesser detector specific, the later they are located in the processing 

chain, where the first unit, the preamplifier is highly detector specific and is typically co-developed 

with the detector architecture and geometry, while the back-end serializer and drivers are typically very 

generic and not detector specific at all. This observation leads to the goal to build as much as possible 

generic building blocks, with the appropriate customization options, in order to allow moving the border 

between generic and detector specific development as far as possible to the front of the electronics chain. 

One option could be the definition of a generic multi-purpose ADC with a defined analog input, to which 

all detector specific preamplifiers are designed. Another even more ambitious approach could be the 

design of a customizable multi-purpose preamplifier, suitable for multiple detectors. 

Another important aspect of the CBM experiment is the elimination of the canonical trigger hierarchy. 

In CBM all front-end systems implement appropriate hit detection circuitry, which selects relevant information 

and ships it together with appropriate time stamps off the detector independently of all other 

systems. Consequently there is no maximum latency requirement, except for the selection of the readout 

of neighboring channels around a given hit. This architecture opens a large degree of freedom because it 

may be pipelined to a high level.

9.1. General Considerations, Requirements 197 

9.1.1 Considerations with respect to electronics 

The requirements for the front-end electronics, sketched above, necessitate the application of microelectronics, 

for the first signal processing and readout stages, in particular due to the channel density and in 

part due to the channel count. One aspect to be considered in this context is cost. On one hand the mask 

cost for modern deep submicron processes is around $300k for a 180 nm process and approaches $1M 

for a 130 nm process. Multi project wafer (MPW) runs allow to mitigate this high cost by synchronizing 

multiple R&D chips and merging them onto one reticle, therefore allowing to share the mask cost. Such 

MPW runs typically yield 50 to 100 chips with the option to buy 1. . . 2 times that quantity in addition 

for testing. To dates prices range in 180 nm technology between about $10k for a 9 mm 2 chip and about 

$35k for a 25 mm 2 chip. The appropriate cost for the 130 nm technology exceeds a factor 2 of these 

numbers. 

Assuming the lower cost 180 nm process and a minimum of one prototyping run, generates non recurring 

engineering NRE cost of $335k per run. Applying these numbers to the CBM TRD detector and 

assuming a grouping of 16 channels per chip, results in about $9 per chip. Note the ALICE TRD Tracklet 

Processor chip (TRAP) [180] has a per die cost of about $6, assuming a yield of 80%. This chip implements 

22 ADCs for 18 active channels, 21 multi stage digital filters, event buffers, four 32-Bit RISC 

processors and a simplified 4-port 2.5 GBit network switch on one chip. When comparing these numbers 

the NRE cost here exceeds the chip cost by 50% or a factor 45 in case of the ECAL sub-detector, 

assuming chips of similar size. 

Figure 9.2: Number of ASIC prototyping 

cycles in comparison with 

required chips for the LHC project. 

Each histogram bar corresponds to 

one particular chip project. Note 

that there is no correlation between 

the number of required chips and the 

number of prototyping cycles 

Another related issue is the number of MPW runs required before the production run is launched. Given 

the already very high ratio between NRE and production cost, it is likely to have a larger number of 

MPW runs prior to the production run, in order to minimize the risk. Fig. 9.2 shows a compilation of all 

chip developments for the LHC project at CERN [181]. The number of MPW runs, which were required 

for each given chip, are compared with the number of chips required for each given type. There are more 

than 75 different chip developments, requiring between 1 and 14 prototyping cycles. Note the number of 

prototyping cycles is not related to the number of chips required in production. Most chips are developed 

with two or three prototyping cycles. However these prototyping cycles will add about $100k per chip 

project in NRE. Note further that the number of chips required for any detector at CBM, for instance 

about 10k for the TRD assuming a 16 channel chip, is at the low end of the spectrum in Fig. 9.2. 

Taking the above observations into account the consequence is to minimize the number of different 

silicon processes as far as possible with the goal to use one foundry as baseline for all microelectronics 

development. This plan has a variety of advantages. First it is possible to build a library of digital and 

analog building blocks, to be used in the different chips. Further it is beneficial to implement more 

functionality into the chips, allowing them to serve multiple purposes, selected by configuration. The 

typically imposed area penalty will only increase the production cost of the chip by a small amount.


Further it is possible to share MPW runs for different developments, as often the minimum MPW silicon 

buy is larger than the area required for the given test chip. The disadvantage of this approach is the 

complication in schedule for the different detector projects. Finally it is even possible to share the 

production run between different possible chip projects, therefore also sharing the very large production 

cost. 

Such an approach was successfully taken by the ASIC-CC community in the context of the ALICE TRD 

TRAP chip. There were many different developments, which were executed in parallel, sharing the 

few MPW runs (here 4). For instance there is the entire digital processing and readout back-end, the 

development of a 10 MSPS, 10-Bit ADC, a 2.5 GBit serdes, a temperature sensor, instrument amplifier, 

specialized low-power LVDS cells, reference voltage generators, and the like. The final production reticle 

includes 15 TRAP chips, 1 ATOLL chip (high-speed PCI-X network chip for the ALICE HLT) of same 

size and four OASE chips (2.5 GBit serdes with digital front-end). The ratio of TRAP and ATOLL was 

chosen to match roughly the number of required chips. The TRAP chip itself can be operated as three 

different chips with different configurations, depending on where it is mounted inside the detector. 

It should be noted here that one important candidate to be considered in the area of the front-end electronics 

is the FPGA. FPGAs utilize the most modern silicon technologies, due to the same reasons as 

DRAMs do. They have a large number of regular building blocks and allow therefore a much quicker 

adoption to new silicon processes than for instance microprocessors do. We may safely assume that the 

per gate cost in FPGAs will continue to decrease and the internal clock rates will increase. Therefore 

it is likely that purely digital designs may be better integrated in FPGAs, possibly using their gate array 

option for cost reduction, unless some features are required, which are not available or are unlikely 

to become available in the context of FPGAs. The technological development of FPGAs will have to 

be monitored very carefully during the course of the development of the CBM detector (refer also to 

section 9.10.2). 

9.1.2 Microelectronics technology choice 

Several Application Specific Integrated Circuits (ASICs) will be developed for the particular requirements 

of CBM. In order to share expertise and building blocks (’IP cells’) among the various designers 

involved it is the goal of CBM to use the same technology for the majority of the designs (see also 

figure 9.2, section 9.1.1). 

Most groups involved so far in the development of integrated front end and readout electronics are 

presently using the 0.18 µm technology from UMC. This technology is therefore the most obvious candidate 

for a baseline. Moving to another technology requires strong arguments. Going ’back’ to feature 

sizes of 0.25 µm or more does not seem to be reasonable and feasible, as certain designs (serial links, 

high resolution TDC, ADC) require the speed of a 0.18 µm technology. Moving to technologies smaller 

than 0.13 µm does not seem to be required. These very modern technologies are difficult to access and 

are probably not affordable at this time. The following three options for a baseline are therefore possible: 

1. stay with UMC 0.18 µm; 

2. switch to a 0.18 µm technology from an other vendor; 

3. move to 0.13 µm. 

The first option is favored by CBM at this point in time for the following reasons: 

• Existing expertise. As mentioned above, the groups involved have a running design flow for this 

option. The typical initial problems when installing a new design kit have been solved.

9.1. General Considerations, Requirements 199 

• Existing IP cells. Several existing building blocks can immediately be used. Examples are given 

in the next section. 

• Technology characterization. The technology is already well characterized through measurements 

on test structures. 

• Cost. For the same functionality, the cost (UMC trough Europractice) of 0.13 µm increases by 

roughly 50%. Extra overhead is introduced by the more or less fixed size of bonding pads, so that 

pad limited designs become significantly more expensive. 

• Analog Design Possibilities. Analog circuit design becomes more difficult in some respects as 

feature sizes shrink due to reduced dynamic range and increased leakage current. 

• Run schedule. Slightly more runs are scheduled for 0.18 µm (6 vs. 4 per year) so that prototyping 

designs can be submitted easily. There are also slightly more Mini-Asic runs (3 vs. 2 per year). 

• Radiation hardness. Although the exact doses expected at CBM are not yet well known, we 

believe that the radiation hardness of designs in a 0.18 µm technology even without any special 

precautions is sufficient. 

• Layout density is roughly doubled for standard cells when moving from 0.18 µm to 0.13 µm. We 

do not see this as an advantage by itself. However, pad limited designs do not benefit from the die 

shrink because bond pad size remains constant for packaging reasons. Further analog designs also 

may not fully utilize the full area saving. On the other hand at this point in time the cost for MPW 

runs is twice the cost of a comparable 0.18 µm process. 

• Routing Layers. More advanced technologies also offer more routing layers (8 vs. 6 for UMC). 

We believe, however, that the six layers available in the 0.18 µm technology are enough for the 

designs we envisage. 

• Power consumption. The power consumption at a given circuit decreases for smaller feature size 

due to shrinking capacitances. We do not, however, expect power consumption to be a major 

problem in this fixed target application. 

• Speed. Designs become faster with shrinking feature sizes. In this context also copper processes 

have to be considered, due to its higher conductivity it allows faster interconnect routing. Copper 

starts becoming important at feature sizes of about 0.18 µm. However, we are confident, that our 

design goals can be met with the present technology. An exception may be a TDC with a resolution 

of a few ps only. 

It should be noted that CERN has chosen a 0.13 µm technology as the baseline for future developments 

thereby replacing a 0.25 µm process. With the decision to drop a technology which is considered to 

be not sufficient anymore, CERN goes a significant step (of a factor of roughly four with respect to 

the 0.25 µm process) further. The situation in CBM is different, because the technology step would be 

relatively small. Note also that LHC++ will start later than FAIR so that a longer term perspective is 

reasonable. 

CBM retains the option to transition to a more advanced technology, if really required, after 3-4 years 

of R&D when the experiment’s requirements are well understood and most of the critical electronics 

building blocks have been designed. An appropriate die shrink step can be implemented in a timely 

manner.


9.2 Existing microelectronics building blocks 

The development of microelectronic circuits has a significant overhead with respect to the maintenance 

of the software and design libraries as well as the requirement to study and test certain process features, 

which may not be well represented in the appropriate libraries of the foundry. In the context of the 

ALICE TRD project a group of four institutes has joined forces in the ASIC Competence Center (ASIC- 

CC) and has developed a variety of modular ASIC building blocks, which may be reused in the context 

of the CBM project or serve as starting point for improvements and customizations. An appropriate list 

of available building blocks is compiled below with a brief description of the devices. All listed building 

blocks have been built and tested. The developments have centered around the UMC 0.18 µm process, 

where some are in 0.13 µm for further reference. 

9.2.1 TRAP-ADC 

The TRAP-Chip of the ALICE Transition Radiation Detector required a 10 bit 10 MS/s-ADC with the 

shortest possible conversion latency, to be integrated in a multichannel array together with the complex 

digital electronics of this signal processor. 

Figure 9.3: Photograph of a single TRAP-ADC. Dimensions: 

200 µm × 550 µm 

The stringent requirements in terms of power and chip area were met with a cyclic ADC-architecture, 

which turned out as optimal solution for this application. At 10 MS/s the power consumption of a single 

TRAP-ADC is 12 mW, a value close to that of other state of the art ADCs published. 

Figure 9.4: Effective number of bits (ENOB) 

and signal-to-noise ratio (SNR) versus input frequency 

of the TRAP-ADC 

Figure 9.5: Output spectrum of the low-power 

version @ 100kHz signal frequency. The signal 

frequency was limited by the test setup. 

Cyclic ADCs use the same conversion principle like pipeline ADCs, but consist of only one stage which 

performs the quantization in a sequential manner. As recent pipeline ADCs in 0.18 µm CMOS reach 

100 MS/s, the maximum achievable throughput at 10 bit resolution is in the order of 10 MS/s, while the 

converter area is greatly reduced compared to a pipeline implementation. The photograph of a single 

TRAP-ADC, which occupies only 0.11 mm 2 of silicon, is depicted in Fig. 9.3.

9.2. Existing microelectronics building blocks 201 

Parameter TRAP-ADC Low-Power version 

Resolution 10 bit 10 bit 

Conversion rate 10 MS/s 10 MS/s 

Active area 0.11 mm 2 0.11 mm 2 

Power consumption 12 mW @ 10.4 MS/s 7 mW @ 10.4 MS/s 

Input range ±1 V – ±1.4 V ±1 V 

DNL –0.4/+0.6 LSB not analyzed yet 

INL –0.8/+0.7 LSB not analyzed yet 

ENOB 9.5 @ 1 MHz input 9.1 @ 100 kHz 

SNR 59.2 dB @1 MHz input 56.4 dB @ 100 kHz 

SFDR 73.0 dB @ 1 MHz input 63.0 dB @ 100 kHz 

THD -69.5 dB @ 1 MHz input -74.2 dB @ 100 kHz 

Table 9.1: Listed data of the TRAP-ADC and its low-power version 

Various advanced design techniques like a second set of sampling capacitors to enlarge the sample interval, 

conversion cycles of different lengths or a pseudodifferential implementation to save power were 

exploited to obtain this ADC [182]. 

In Fig. 9.4 the effective number of bits (ENOB) and the signal-to-noise ratio (SNR) versus input signal 

frequency are shown. The ENOB stays well above 9 bit over the whole Nyquist frequency range. 

Due to the tight project schedule of the TRAP-development the TRAP-ADC was in many points rather 

conservatively designed in order to ensure a reliably working circuit. The knowledge gained during 

designing and testing the TRAP-ADC allowed to implement a more improved version with lowered 

power consumption. Though it has not been completely tested yet, its first performance data in Fig. 9.5 

and table 9.1, right column, are promising. The power consumption is 7 mW @ 10 MS/s. This ADC 

shows the possible power efficiency of similar converters in the future. 

Table 9.1 summarizes the performance data of the TRAP-ADC and its low-power version. 

9.2.2 LVDS IO Cells. 

These cells are layout compatible to the digital standard IO cells. They can operate at up to 500 MHz 

at a power consumption of 7.4 mW for the transmitter and 0.5 mW for the receiver. Both transmitter 

and receiver can be disabled, putting them into a low power mode. An enabled receiver, being driven 

by a disabled transceiver will generate a defined ZERO at its internal outputs, therefore allowing the 

dynamic enabling and disabling of the LVDS transmitters without generating any side effects at the 

receiver end, provided the default signal state is defined as ZERO. There is a second set of LVDS standard 

I/O cells, which incorporate the complete boundary scan logic, drastically simplifying the boundary scan 

placement and routing. 

9.2.3 On-Chip Slow Control 

For modern high complex integrated read out electronics it is very helpful to have access to several 

system parameters as core voltages and temperature. 

For this reason an analog multiplexer and switched capacitor attenuator was designed for the TRAP chip, 

which is available as standard cell in the UMC 0.18 µm-CMOS technology. The cell provides eight fully 

differential analog inputs that could be attenuated by factors of 1, 3/4, 2/3, 1/2, 1/3 and 1/4.


Using the switched capacitor technology in the attenuator provides minimum power consumption during 

operation and even zero power in stand by mode. 

9.2.4 Analogue multiplexer, auxiliary ADC, temperature sensor and voltage reference 

For monitoring on-chip supply voltages, temperature and reference voltage, an auxiliary ADC was combined 

with an analogue multiplexer. This IP-cell can be placed manually and routed to several test points 

of the system on chip. The auxiliary ADC runs at reduced clock frequency and power as required. The 

multiplexer offers several attenuation factors corresponding to capacitor ratios, which can be selected by 

the slow control unit. The IP-cell was verified and successfully tested on the TRAP chip. 

The on chip temperature sensor relies on the change of a pn-junction voltage at constant current. The 

signal is amplified relative to a band-gap reference voltage, using a continuous time instrumentation 

amplifier. The measurements have shown a useful temperature range from -30 to +120 degree Celsius. 

Scale is fairly linear; the resolution obtained using the multiplexer/ADC-cell is approx. 0.2 degree. 

The offset of the scale has sufficient reproducibility for monitoring purposes, although no provisions 

for trimming have been included. The application could perform global numerical trimming in order 

to compensate for manufacturing tolerances (i.e. offsets, mobility, implant). Fig. 9.6 shows a typical 

measurement of the temperature sensor taken on the TRAP chip. 

Voltage [mV] 

600 

400 

200 

−40 −20 0 

20 40 60 

−200 

Temperature [deg.C] 

−400 

−60 

−800 

−1000 

T=(Code−287)x(40/241) 

Figure 9.6: Measurement of the temperaturesensor 

output 

VBandgap[V] 

1,38 

1,36 

1,34 

1,32 

1,3 

1,28 

1,26 

1,24 

1,22 

−40,000 −20,000 

1,2 

0,000 20,000 40,000 60,000 80,000 100,000 120,000 

Tem p[Celcius] 

Figure 9.7: Measured bandgap voltage versus 

temperature 

The on-chip reference voltage generator is a classical bootstrap band-gap voltage source, using multiples 

of pn-junctions in an n-well as reference devices. The output voltage is optionally filtered by the low-pass 

formed by the 100k Ohm output resistance and an external capacitor which is connected between output 

pin and dedicated ground return pin. Thermal noise can thus be largely suppressed. No provisions for 

trimming are implemented, still the variation of the nominal 1.26 V output voltage measured on a series 

of chips was less than one percent. The measurement over temperature, depicted in Fig. 9.7, was made 

with the latest TRAP chip. 

9.2.5 Digital Filters 

The digital filter within the TRAP chip is separated into five functional parts. Within the filter two 

additional decimal places for number representation are used to reduce numerical errors. 

A nonlinearity correction filter performs a look-up table based common correction of the ADC signal. 

The signal is moved to a configurable baseline by a pedestal correction filter. Therefore the individual 

baseline level of each channel is determined by a first order low band pass filter with various time constants 

of 0.8 ms to 0.8 s. A gain correction filter scales the signal in each channel by an individually

9.2. Existing microelectronics building blocks 203 

choosable factor. These factors have to be determined by the on-chip CPUs performing basic spectral 

analysis based on histogramming counters which are included into the data chain. Tail cancellation is designed 

as a second order recursive IIR filter with variable parameters. Crosstalk suppression is available 

as a fourth order FIR filter. 

9.2.6 High-speed Tracklet Processor 

In this block of the TRAP chip the signal is parameterized by a set of track segments (tracklets). Therefore 

a maximum of four clusters per time step can be detected and included into the associated tracklet. 

The two coordinates of a cluster are drift time and pad position which is calculated by using charge 

sharing between adjacent channels. Tracklets are built channel-wise by accumulating the parameters of 

a straight line fit and charge in various sections of the drift time. 

9.2.7 32-bit RISC CPU 

The CPU-core is 32-bit RISC processor, impelementing a two stage pipeline and some special components 

(Fig. 9.8). The Fit Register File (FRF) is the interface to the Preprocessor. The Global Register File 

(GRF) is the fastest way to exchange information between the 4 CPU cores and to synchronize them. The 

fixed and programmable Constants can be used like registers. The Arbiter gives access to the IO devices, 

shared by the 4 CPUs and the SCSN interface (section 9.2.12). The instruction memory is private 4k x 24 

+ hamming bits. The data memory is quad ported, 1k x 32 + hamming. 

5 

5 

4 

4 

3 

D D 

IMEM 

DECODER 

PIPE1 PIPE2 

WE 

WA 

WD 

RA 

GRF 

RD 

PRF 

WDAT 

C C 

ADDR RDAT 

WE 

WA 

PRF15 

WD 

devices 

RA_A RD_A 

RA_B RD_B 

MEM read 

FIT 

RA_A RD_A 

RA_B RD_B 

B B 

r/w 

PC 

IMM 

IMM 

PRF_A 

PC+1 

ShadowPC 

RA 

CON 

RD 

PRF_A 

GRF 

PRF_B 

PRF15 

IMM 

PRF_B 

GRF 

r/w 

A A 

3 

IO write data 

ALU 

r 

w 

2 

IO read 

MEM address 

PRF_A 

IO address 

2 

Counter/ 

timer 

IRQ 

contr 

Arbiter 

other 

QPDMEM 

ADDR RDAT 

WDAT 

Figure 9.8: Block diagram of the CPU core 

9.2.8 Network Interface 

1 

1 

local I/O 0 

local I/O 1 

local I/O 2 

local I/O 3 

global I/O 

A0 

Di0 

Do0 

A1 

Di1 

Do1 

A2 

Di2 

Do2 

A3 

Di3 

Do3 

AG 

Di4 

DoG 

Network 

Interface 

I/O 0 

I/O 1 

I/O 2 

I/O 3 

I/O G 

16 

16 

16 

16 

port 3 port 2 port 1 port 0 

16 

FiFo 

128x16 

10 10 10 10 

16 16 16 16 

16 

FiFo 

128x16 

16 

FiFo 

128x16 

16 16 16 16 

16 

FiFo 

128x16 

endsig endsig endsig endsig 

16 16 16 16 

16 

port 4 

Figure 9.9: Network Interface block diagram 

Given the large detector surface area and number of 65000 MCMs to be readout, the best architecture 

is to implement a multi-level readout tree. The resulting 4-to-1 readout trees, which collect the data 

of 64 MCMs each, are build up by a Network Interface (NI), which is a part of the tracklet processor 

chip. The NI consists of four 10 Bit wide input ports, one output port, FIFOs, interfaces to the processor, 

and control logic (Fig. 9.9). The data transfer is performed with 120 MHz DDR, therefore running at 

2.5 GBit/s. In order to save power the NI is capable of operating stand-alone and dynamically switching 

off unused LVDS ports. 

10


9.2.9 High Performance ALU 

The ALU applies arithmetic and logic operations on two 32-bit integer operands. It implements binary 

logic (and, or, xor) and the standard set of typical integer arithmetic operations. In addition, bit shifts of 

variable distance can be applied. The implementation allows for high clock rates, while still performing 

most arithmetic operations in a single clock cycle using a non-pipelined architecture. The selected 

implementation for the divider is a high-performance CSA-based radix-4 divider. 

9.2.10 Quad Port Memory 

The quad port memory is the data memory for the four RISC CPUs of the Trap Chip. It allows four 

concurrent accesses simultaneously. Each port can be used independently for read or write access. Data 

written via one port is also available via all other ports. This memory is an ideal place for mutual data 

exchange between the four CPUs on the TRAP die. It fills an area of 1.36 mm 2 and offers a capacity of 

1024 words. Each word has 32 bits plus 7 hamming bits for error correction. 

9.2.11 Programmable Delay Unit & Network Fault Tolerance 

All output signals can be shifted in multiples of ∼1 ns (Fig. 9.10) with respect to the reference clock. 

This Programmable Delay Unit is designed to equalize skew caused by different trace lengths on die, 

PCB or interconnect. Its dynamic range covers a full clock period of 120 Mhz. 

Propagation delay (ps) after Delay Unit 

9000 

8000 

7000 

6000 

5000 

4000 

3000 

2000 

D 

LH bit3 single 

HL bit3 single 

LH bit3 

HL bit3 

SERIAL 

S0out S1in 

D 

1000 

SLAVE 

S0in S1out 

1 5 

0 

0 1 2 3 4 B5 6 7 

ring 0 

ring 0 

B 

delay selected in Delay Unit 

Figure 9.10: Measured results of one delay 

channel for different transitions of the channel 

itself and its direct neighbors. 

5 

5 

4 

ring 1 

4 

3 

C C 

3 3 

S0out S1in 

MASTER 

SLAVE 

5 1 

SLAVE 

4 2 

SLAVE 

SLAVE 

2 4 

A A 

3 

S0in S1out 

BRIDGED 

2 

2 

MASTER 

Figure 9.11: SCSN architecture, implementing 

two anticyclic rings (left). Bridge operation, 

eliminating single points of failure 

Dedicated test patterns allow to optimize the eye pattern for each link. Normally the link consists of 

8 Bit data, 1 Bit parity and 1 Bit spare. The position of the spare bit can be configured within the 10 Bit 

port. In addition the parity bit can be excluded to get two redundant bits. However, in this case no error 

detection can be performed. 

9.2.12 SCSN Redundant High-speed Configuration Network 

The Slow Control Serial Network (SCSN) transmits data packets at 24 MBit/s speed. It is organized as 

two antizyclic rings, allowing to address up to 126 slaves in one ring. If some connection is broken, 

the second ring can be used. If a node fails full connectivity to the remaining nodes can be obtained 

by configuring the operational neighboring nodes to perform a loop back operation, therefore breaking 

up the two redundant large rings into two independent small rings (refer also to Fig. 9.11). The SCSN 

ring 1 

1 

1

9.3. Overall Architecture 205 

protocol implements fixed length packets with a payload of 32 bit data, 16 bit address, CRC check. It implements 

remote shared memory commands, including special network commands, such as ping, bridge, 

broadcast. The broadcast commands are reliable, and are used to initialize common data structures, such 

as the instruction memories of the front-end TRAP chips. 

9.3 Overall Architecture 

The sections above outlined the requirement for a mostly generalized front-end electronics. It is pointed 

out again that the majority of the cost will be in the non recurring engineering and not in the chip cost 

itself. Further should be noted that the experience, gained in the ALICE TRD project, where the channel 

count exceeds the CBM TRD detector by a factor two, also suggests to consider trade-offs between the 

chip size and the integration cost. For instance if the bonding PAD pitch is chosen too small then the 

cost for the chip package or carrier will increase dramatically, while a slightly larger chip may already 

support the use of low-cost PCB material, using a feature size of 110 µm, as carrier (CoB). In such 

cases it will be very cost effective to design somewhat larger chips in favor of the reduced integration 

cost. For instance in the ALICE TRD project the integration cost of the TRAP chip (bonding, glob-top, 

BGA soldering) amounts to about 8.9 (26% NRE, 15% consumables, 59% manpower) while the chip 

cost itself is at about $6. Note the largest integration cost components are man power and NRE, which 

typically increase as the production unit count decreases. Note further that the cost per silicon real estate 

is about 10 Cents without yield correction. 

Given these considerations it is attractive to aim for one or a few mostly generic chips, that can serve 

an as large as possible set of purposes within the CBM detector. Such generic chips will include functionality 

of several independent functions which may never all be enabled simultaneously, resulting in 

any configuration having disabled areas on the chip. This, however, is not considered a disadvantage as 

the arguments above drive towards larger chips anyway and such configuration options may use silicon 

real estate, which would otherwise be used for filler structures. A detailed analysis of these trade-offs 

remains until the detailed detector requirements are finalized. 

The CBM detectors do not operate a classical trigger/readout architecture. Instead all detector channels 

implement some hit selection functionality, generating a data stream of time stamped hits, which are then 

coalesced within the higher layers of the readout system. In this context it has to be noted that on one 

hand this coalescing will have to merge several events into one container, for instance since there are 

slow detectors (e.g. MAPS), which cannot resolve hits at resolutions down to nanoseconds and since in 

order to avoid granularity losses the hits with time information close to a time granularity border have to 

be replicated. In addition this trigger less architecture requires a very precise time stamping information 

for all fast detectors in order to minimize the combinatorial background when putting together hits to 

events. 

Further all front-end modules have to have precision time information, in order to allow proper hit matching. 

Given the average event rate of 10 MHz a hit time resolution of 1 ns appears adequate for the hit 

identification. The hit time should be adjusted for the various detector channels, such that particles at 

c = 1 have the same time stamp. 

The front-end hit selection has to implement some next neighbor functionality in order to allow the 

readout of next neighboring channels below the threshold in order to digitize the entire hit cluster. Depending 

on the cluster size one approach could be to implement preamplifiers with multiple outputs per 

channel, which would be routed such to different digitization chips, that under all circumstances at least 

one digitization chip has a complete cluster available. This approach was chosen for the ALICE TRD. It 

heavily relies on the number of next neighbors being very low and the corresponding cluster size being 

small. One consequence is power and cost overhead for the additional digitizer channels. In case of the


ALICE TRD this overhead is 17%. However, this architecture cannot be a general solution. Therefore 

it is required to exchange the information of a given hit to the appropriate chips, processing neighboring 

channels. In general there are two principally different approaches. One is to just exchange some 

kind of hit signals between the next neighbors, which trigger the readout independent of its hit status 

if a neighbor has a signal identified as hit. The second option uses the CNet readout infrastructure to 

distribute the hit information to the next neighbors, using some kind of low latency multi cast. Latency 

is very important here as all hits have to be stored for that amount of time. Assuming a canonical detector 

readout system, results in readout link lengths of several tens of meters, resulting in latencies of 

hundreds of nanoseconds. High-speed switches have propagation latencies of hundreds of ns, ranging 

up into the µs range, while assuming zero load. At a digitization rate of 50 MHz this corresponds to 

50 time bins. However, in the example discussed we may not assume the appropriate switch to be idle, 

as it may happen that a large number of neighboring detector channels are hit simultaneously. Under 

all normal circumstances there will be a load dependent delay in the appropriate switches, requiring an 

appropriately large input buffer (refer also to section 9.9). 

It should be noted also, that in any case some buffering of the analog signals is required in order to access 

the leading edge of a detector signal, when a hit is detected, for instance by some peak sensing logic. 

However, it is often not possible to combine physically adjacent detector channels on the same readout 

board, making it difficult to exchange the hit information in this scenario. Given the arguments presented, 

the next neighbor hit selection is implemented wherever possible by generating hit messages and 

directly distributing them to the appropriate next neighbor chips, which reside on the same or adjacent 

readout boards. Neighboring channels, which cannot be handled in this manner receive their neighbor 

hit information via higher levels of CNet, which will only see a small fraction of the resulting message 

transfer, as most of it is done on the readout boards. This message transfer puts very high absolute latency 

requirements on the CNet architecture. Note that this latency requirement may be reduced at the 

cost of power consumption by lowering the digitization threshold very close to the baseline, resulting 

in many low level readout triggers, resulting in the appropriate data being stored in the front-end digital 

buffers, allowing a loss less storage of this information for a longer period of time. For a hit trigger 

to be communicated to neighboring channels, however, an appropriate second threshold will have to be 

met. The baseline data is readout only if a matching hit trigger is received. It will be discarded after 

the maximum hit trigger time-out has been reached. Appropriate discrete event simulations remain to be 

done in order to model this system. 

Every detected hit has to be time stamped with a time resolution of about 1 ns. This resolution is not 

good enough or intended for any time-of-flight measurements but only used to disentangle the various 

hits inside the detectors. A time measurement with this resolution is very feasible to be implemented 

purely using digital standard cells and therefore does not present a particular technological challenge. 

Another side effect of the elimination of the trigger signal is the increased requirement for noise reduction. 

While in triggered systems effort may be implemented for the trigger detectors in order to not fire 

in case of noise hits and therefore suppressing any noise in any other detector outside the trigger window, 

the given architecture will readout any noise hit. Two strategies will have to be deployed. First the hit 

detection on the detector within the front-end electronics will already have to implement somewhat more 

advanced hit detection as usual. Further the hit merging functionality at the higher levels of the readout 

chain will have to identify isolated hits, likely to be noise, and discard them. 

Fig. 9.12 sketches the building blocks required for the CBM front-end electronics. The first stage is the 

preamplifier. By defining appropriate electrical and physical interfaces it is possible to have multiple 

preamplifier standard cells, which may be tuned for the specific detector and inserted into reserved areas 

on the chip during the tape-out phase. In order to gain maximum independence between the sensitive 

analog preamplifiers and the higher stages of the electronics chain on the chip all signals are planned to be 

done differentially. These adaptive filter blocks are optimized to fit the following ADC stage. The second


Ain 

1..N 

N Pre- 

Amplifier 

Blocks 

. 

. 

. 

Hit Detector 

• Leading Edge 

• peak 

• … 

DAC 

Analog 

Filter 

to/from next 

neighbor hit 

detection 

to/from next 

neighbor hit 

detection 

Programmable 

Finite State 

Machine 

TIMER 

CLOCK 

WE RE 

ANALOG 

FIFO 

start 

Hit TDC 

stop 

sample 

ADC 

different combinations 

• sampling rate 

• resoluion 

• power 

Digital 

Pipeline 

Digital 

Filters 

Figure 9.12: Overview of the required FEE building blocks 

send 

Fast 

Readout 

we 

Event 

Buffers 

DATA-out 

stage are analog filters, implementing necessary analog functionality, including an anti aliasing filter. The 

analog signals are subjected also to an analog hit detector, which is sketched in a most simple form as 

single, programmable threshold leading edge discriminator. Depending on the detector requirements it 

may become required to implement more advanced hit detectors, such as peak detectors and the like. 

The hit detectors generate a hit signal, which may be distributed to the next neighbors. The hit signal is 

also ORed with the appropriate hit detection signal of the neighboring FEE chips, triggering the readout 

of the channel. 

In order to allow the readout of the pre-history, the analog signal has to be sampled at all times. Since the 

analog sampling requires less power and infrastructure than the constant walk digitization and storage 

in cyclic buffer memories, an analog switched capacitor array is the best solution here. Refer to section 

9.5 for a discussion of the various options here. In case of a hit the ADC is enabled, recalling it from 

its low-power stand-by mode. It then digitizes the pulse, starting at time bin thit −tprehist in order to also 

capture the prehistory. A similar technique is deployed in all neighboring chips. 

The time of the hit is measured by a TDC with respect to the next clock edge. In order to save power the 

TDC is implemented as common stop TDC, leaving all channels without hit in stand-by mode. The hit 

time is associated with the hit and subsequently transmitted with the digitized analog information. 

The analog-digital conversion is performed by a highly configurable ADC (refer to section 9.6). It is 

followed by appropriate digital filters. Unlike in case of triggered detectors where the ADCs have to be 

operated at constant walk in order to ensure correct operation of the digital filters, since some hits may 

otherwise be excluded from the digitization, since they were outside the trigger window, all relevant hits 

are being digitized in this context. Therefore it may be possible to operate digital filter with digitization 

duty cycles ≤100%. It should be noted that the fall back position to operate the ADCS in constant walk 

remains a configuration option. A particular baseline restoration filter will have to be developed in order 

to cope with the architecture presented here. 

The hit information, which is not required for the digital filters has to be delayed with an appropriate 

pipeline to match the propagation delay of the digitized analogue data in the digital filters. At the filter


output the hit/time data is queued in a derandomizing buffer for subsequent readout off the detector using 

some high-speed signal transmission system (refer to section 9.8.2). The size of the derandomizing 

buffers remains to be simulated in detail, depending on the detector granularity and corresponding statistical 

hit and data rate fluctuations. However, a buffer size of 8. . . 16 average events with the capability 

to store at least one black event, appears to be sufficient. 

The building blocks outlined above will be developed as independent units with well defined interfaces. 

However, the goal on the long run is their integration on one larger multi function, multi purpose chip. 

However, even if this goal is not achieved, the existence of generic building blocks, which are ready to be 

used in different chips, with customized preamplifiers, still serves the purpose of reduced development 

cost, time and risk. 

9.4 First Amplifier Stages 

The first amplifier stage, the preamplifier/shaper (PASA), resembles the most detector dependent component 

in the entire electronics chain. Often it has to be codeveloped together with the detector architecture. 

Different scenarios are conceivable, ranging from entirely independent preamplifier chips, possibly even 

being integrated in a different technology, up to the integration of the entire electronics on one die. In 

any case a well defined interface defining the analog output of the preamplifier, is required. 

The conventional approach is based on separate ICs for the preamplifier and the digital electronics, 

including the ADCs. This approach was chosen in case of the ALIC TPC and TRD electronics, there 

for the first time the ADCs and the digital back-end were integrated on one die. In this scenario the 

preamplifiers have single ended inputs, connected to the detector electrodes. This is a practical solution, 

providing the required flexibility with respect to different detectors. Disadvantageous is, however, the 

large space for board connections, large load capacitance and cross talk at the outputs of the PASA. 

Spurious noise at the inputs of the PASA can result from the single ended connections to the chamber. 

Interference of signal and power ground is difficult to suppress. This requires a very careful board design. 

In any case differential coupling to the ADCs is used to cancel common mode noise. 

The proposed approach is based on a single chip solution. This implies the necessity of differential 

inputs, in order to provide sufficient isolation of signal ground and power ground. Further goal is an 

extended sampling rate of the ADC, which should be variable as an option. This implies a variable antialias 

filter in the preamplifier. Assuming a maximum specified sampling rate of 40 MHz and a 12 bit 

dynamic range, the design of the low power analogue amplifiers and buffers is a real challenge with 

respect to signal to noise ratio, bandwidth, linearity and dynamic range. More relaxed specifications will 

result if we assume a dual-range solution of PASA and ADC. This will be referred to as a recommended 

solution in the ADC section of this document. A draft schematic of an integrated PASA/ADC section of 

the proposed system is given below (Fig. 9.13). It is based on a modular design, which offers sufficient 

flexibility for the different detector applications. 

The preamplifier is a generic module that takes specific requirements of the detectors into account. The 

depicted example of this module is typical of a drift chamber detector, with positive impulses of displacement 

currents at the input. The feedback capacitor has to be chosen with respect to pad-to-plasma 

capacitance, dynamic range and noise figure. Obviously the loop gain of this stage must be very high, in 

order to provide an input impedance as low as possible (virtual ground). This is not easy to achieve, as 

the envisaged bandwidth of the system is in opposition to the transit frequency/power limitations of the 

CMOS circuits. The design task is therefore closely related to the chamber specifications and requires 

individual optimization. However, we can allow for some general constraints, like defined cell pitch and 

defined load capacitance drive at the (single ended) output. The use of a differential configuration for the 

1 st stage is a also general requirement, which allows to connect the signal return of the inputs directly to

9.4. First Amplifier Stages 209 

Detektor 

pad 

Signal 

ground 

Detektor 

shield 

Preamp 

Power 

ground 

Comparator 

Anti−Aliasing 

and Zero 

g −C m 

Filter 

{ 

t gr 

D/A 

x1 

High range 

x8 

Range 

Low range 

clk 

9bit 

ADC 

Range 

bit 

9 

Data 

out 

4 

Digital control 

of corner 

frequency 

clk 

Figure 9.13: Proposed 

Preamplifier-Shaper and 

ADC 

the chamber ground without the interference of supply currents of the electronics. Dedicated preamplifiers 

that meet these requirements will fit into the system. Detector related inversion of signal polarity 

will be accounted for in the following filter and buffer stage. 

The filter stage contains typically a first order high pass and a higher order low pass filter. As an option 

it is aligned with the sampling frequency of the ADC. Over-sampling at high frequencies is not feasible, 

thus we have to use a continuous time filter. Classical RC active filters are limited in performance at the 

envisaged high frequencies, and they suffer severely from the large RC product tolerances on the chip. 

Their trimming to variable cut off frequencies is very difficult to achieve. A more attractive candidate 

is the class of cascaded differential gm-C filters. It has less bandwidth limitations and it can be simply 

trimmed by the variable gm-stages, e.g. digitally by using a DAC-stage. 

Vin 

3rd−order gm−C low−pass filter: 

gm gm C1 

Rin L 

Passive representation: 

Rin/2 

Vin C1 

Rin/2 

gm 

gm 

L/2 

L/2 

CL gm 

gm 

C2 

C2 

Rout 

Rout 

gm 

Vout 

Vout 

Figure 9.14: Cascaded 

differential gm-C-filter and 

equivalent RLC-filter 

Fig. 9.14 illustrates the principle of a possible realization of a 3 rd -order ladder filter in comparison with 

its passive LC-counterpart. Ladder filters are relatively insensitive to component tolerances. However, 

a critical design issue of gm-C filters is their limited linearity of approx. 60 dB THD, corresponding 

to 10 effective bits. Fortunately, in a dual range approach this will be acceptable, since we can reduce 

the dynamic range of the filter stages to 9 bit. The required extended resolution at low signal levels 

is provided by the gain of the following linear (×8)-amplifier before the ADCs sampling gate. The 

sampling of both ranges is done simultaneously on individual sampling gates. 

The low/high- range bit, which signals an overflow of the (×8)-amplifier, can already be generated at the 

input of the filter using a comparator which is triggered by the ADC clock. As the analogue signal is 

delayed by the group delay time of the filter (some 10 ns), the ADC receives the range bit right before the 

clock cycle of the first conversion. Scales mismatch, which is an issue of concern in a dual range system,


can be corrected during the measurements. As explained in the ADC-chapter 9.6, a special mode of the 

ADC can provide redundant conversion of low range events on both sampling gates, one after the other. 

This will allow the repeated correction of the scales by a digital data processor. Drift or aging can thus 

be accounted for. 

Individual preamplifier cells should adopt to the special requirements of the different detectors (like TRD, 

RICH and ECAL). Design engineers and detector specialist have to cooperate in providing the optimized 

cells. There is a common geometrical requirement of those cells, as they have to be interchangeable and 

must fit in the cell pitch. Area can be varied in one direction as needed. A differential input stage, where 

the signal ground of several channels can be combined in one pad, is highly recommended. This helps 

to avoid interference of the signal return path and the noisy supply current path. The rest of the front 

end electronics can be common to many variants of detectors, since the required adjustments of polarity, 

sampling rate, filter cut-off frequency, and resolution will be built in. 

9.5 Analog Memory 

An analog memory between the preamplifier and the ADC offers the possibility to to turn off the ADC 

completely when no event has been detected and thus save power. During such periods the signal of the 

preamplifier is sampled in a field of analog storage cells, a memory. Only when a comparator detects 

a relevant signal level, the ADC is turned on and converts the samples of interest. The analog memory 

bridges the time gap until the ADC runs properly, at least one sample cycle. Furthermore, it allows to 

digitize samples taken before the event had been detected. 

Data lifetime 

[number of sampling intervals] 

100000 

10000 

1000 

100 

10 

0 20 40 60 80 100 120 

Temperature [deg C] 

Figure 9.15: Architecture of a switched capacitor 

analog memory cell consisting of MIMcapacitors 

(top); Expected storage time in an 

analog memory cell versus temperature (bottom) 

Figure 9.16: Analog Memory cells using a 

switched-current technique 

Fig. 9.15 shows a Sample&Hold-circuit, known as “flip-around S&H”, where an amplifier is used to 

sample (write) signals in several analog memory cells. The same amplifier can later in the hold mode

9.6. Multi-purpose ADC 211 

(Read) buffer the sampled signals to be converted by the ADC. With two amplifiers writing and reading 

in different cells could happen at the same time. 

Due to its simplicity the approach of Fig. 9.15 is ideally suited for a smaller number of storage cells, 

below 20 cells. For larger arrays the area of the capacitors (UMC 0.18 µm: 1 fF/µm 2 ) gets relatively 

large, also the stray capacitances of the wiring, which cost power and can limit performance. 

For larger arrays switched current cells like in Fig. 9.16 can be a better solution. Here signal currents are 

stored as corresponding gate charges in transistors. This solution offers much smaller storage cells. On 

the other hand, achieving accuracies of 10 bit or more at the here required samplerates is more difficult 

in switched current technique. The main obstacle is charge injection from the switches, which cannot 

be suppressed so effectively like in switched capacitor circuits, leading to higher harmonic distortion. 

The differential approach of Fig. 9.16 is a first step to overcome this limitation, but still research is 

necessary [199]. 

Fig. 9.15 plots the possible data lifetime in number of sample intervals over temperature. The leakage 

current of the switches degrades the stored signals. Here a degradation of 0.1% was assumed. Faster 

S&H-circuits need larger switches with more leakage current, so the quotient of possible storage time 

and length of the sampling interval is independent of the actual size of the switches. The study of 

Fig. 9.15 was carried out for a switched capacitor circuit, however, for switched current circuits very 

similar results are obtainable. The main result is that at the expected temperatures leakage currents do 

not limit the performance of the proposed analog memory cells. 

9.6 Multi-purpose ADC 

In this section several possible designs of ADCs with resolutions of 10–12 bits and sampling rates of 

40 MS/s are presented. 

9.6.1 General requirements 

In order to achieve the full resolution the maximum jitter of the ADC reference clock is limited by the 

input frequency and digitization resolution. Therefore very low jitter clocks are required in order to 

achieve the high resolution at the given digitization rate. For instance, at 12 bit resolution and 40 MS/s, a 

corresponding 20 MHz input signal frequency, an RMS clock jitter of 2.5 ps leads to an RMS digitization 

error as large as the quantization error (≈0.3 LSB). Higher jitter can easily become the dominant source 

of error for such measurements. 

9.6.2 Variant 1: Interleaving (Multiplexing) of 4 ADCs 

The most simple solution to achieve samplerates of 40 MS/s would be the usage of 4 existing 10 MS/s 

TRAP-ADC (Section 9.2.1), sampling in an interleaved configuration. The cost for the simplicity of this 

approach is a reduced accuracy compared to a single TRAP-ADC, because the individual offsets and 

conversion gains of each ADC lower the overall precision when combining the digitized values from 

different converters by roughly 0.5–1 bit. So the maximum possible accuracy would be clearly lower 

than the target of 12 bits.


9.6.3 Variant 2: Pipeline-ADC with 10/12 bit 

Pipeline ADCs are simple and robust architectures consisting of similar building blocks like the cyclic 

TRAP-ADC. 12 bits of resolution and samplerates around 100 MS/s are state of the art. Fig. 9.17 

shows the first stages of a Pipeline converter in two clock cycles using a technique called “Amplifier 

sharing” [183]. It reduces the number of amplifiers and thus power and maximum throughput by a factor 

of two, therefore the maximum samplerate would be around 50 MS/s here. 

Figure 9.17: Pipeline ADC with amplifier 

sharing 

Figure 9.18: Pipeline ADC in 10 bit mode 

The power consumption of a 12 bit Pipeline ADC @ 40MS/s would be ≈40 mW. A Pipeline Implementation 

can be adopted to a lowered resolution of 10 bits by bypassing the first two stages, responsible for 

the 12th and 11th bit. Fig. 9.18 shows this idea. 

Due to their high precision the first stages consume a reasonable part of the overall current, so turning 

them off would lead to ≈25 mW for 10 bits of resolution @ 40 MS/s. This value corresponds to the 

power efficiency of the low-power version of the TRAP-ADC in Section 9.2.1. Adapting the power 

consumption to a wide range of lower sample frequencies is possible by changing the analog circuitry’s 

currents in a proportional way. Switched capacitor implementations naturally offer this possibility. The 

amplifiers inside the ADC can be designed for a wide range of supply currents. 

• Pro: Flexible in terms of resolution and samplerate (at constant Power/Samplerate) 

• Contra: Pipeline latency of 10/12 clock cycles 

Note that the CBM experiment does not implement the canonical trigger architecture, typically requiring 

a maximum trigger latency due to the finite input event buffers, and therefore requiring short conversion 

latencies. For the CBM experiment the conversion latency is not an important factor. 

9.6.4 Variant 3: 9bit Pipeline-ADC with 2 different gain stages 

An other option would be exploiting the fact that high resolution of the input range is only required in 

a fraction of amplitudes, where the baseline and small pulses are located. Using an ADC of lowered 

resolution, e.g. 9bit, and two preamplifier output stages with different gains allows to digitize fractions 

of the input range with different resolution. 

Fig. 9.19 illustrates this idea. The ADC needs two S&H-inputs to sample always the outputs of both 

gain buffers. A comparator decides, if the signal is located in the low amplitude area where the output

9.7. SerDes, Clock recovery and Optical links 213 

from Shaper 

Comparator 

Anti− 

Alias 

x1 

low gain buffer 

high gain buffer 

x8 

S&H 

S&H 

low 

gain 

high 

gain 

9bit Pipeline 

high/low 

9 

Figure 9.19: 9 bit Pipeline with 

two buffers of different gains 

of the high-gain buffer is to be used to obtain high resolution or if a large signal was sampled, so the 

sampled value of the low-gain buffer has to be digitized. For a high-gain factor of 8 the resolution would 

be increased by 3 bit. 

To overcome inaccuracies in the gains and offsets of the two different gain buffers, a simple digital 

calibration will be needed. In calibration mode the outputs of both buffers are sampled and then digitized 

one after the other. Performing this measurement at different points of the overlap range allows to obtain 

calibration values for the gains and offsets of the two buffers. These calibration cycles can be well 

executed in situ, using the typically implemented electronic test pulser functionality of the preamplifier 

stages. During the digital postprocessing of the data the calibration values are needed for the weighting 

to the corresponding data. The estimated power consumption for this possibility would be ≈25 mW. 

Table 9.2 shows an overview over power and resolution of the different proposed variants of ADCs. 

Variant Power @ 40MS/s Resolution 

Interleaving 48 mW


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block diagrams of Clock 

Multiplier Unit (top) and 

Clock Recovery Circuit 

(bottom) 

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to generate the bit-synchronized clock. Using an adequate phase frequency detector type, recovered 

clock edges are aligned to the center of the data eye opening in lock condition. 

One has to optimize the loop filters jitter transfer function by trade-offs between the different jitter 

sources. Fig. 9.22 presents plots of jitter transfer functions in PLL systems from input and VCO. The 

transfer function of the input has a low-pass filter property. Therefore the output jitter, caused by input 

jitter, can be reduced by minimizing the loop bandwidth. The jitter transfer function of the VCO has 

a high-pass filter nature, hence the suppressed band has to be extended to reduce the jitter contribution 

by the VCO. That means the loop bandwidth has to be maximized, but it is always limited by the loop 

stability. From above discussion, output jitter optimization is a compromise between the jitter from the 

VCO and the input of the PLL system, which is the reference clock in case of the CMU and serial the 

data stream in case of CDR. For instance a clock multiplier unit using a ring-oscillator based VCO needs 

a low jitter reference clock and its loop bandwidth should be wide, in order to reduce the jitter of the ring 

oscillator. Wide loop bandwidth means the multiplied clock can track the quality of that reference clock. 

On the opposite, if a VCO can generate the low-jitter clock, such as an LC oscillator, loop bandwidth has 

to be as low as possible in order to filter the jitter from the reference clock. In this case the quality of the 

reference clock can be relaxed. 

Figure 9.22: Plots of Jitter 

Transfer Functions in a PLL System 

[184] 

The availability of passive RF components such as inductors in CMOS technology improves the performance 

of a ser/des system. Fig. 9.23 shows examples of on-chip inductors. There are many approaches 

utilizing LC-oscillators which can reach 2.5 Gbps or 10 Gbps [185, 186, 187]. Some attempts without 

on-chip inductor have demonstrated multi gigabit data systems, but a high quality local reference clock 

was always required [188, 189].


Figure 9.23: Different implementations 

of on-chip inductors 

[190] 

In the clock distribution system, one approach to generate high quality local clocks is using a QPLL. It 

uses a PLL-based clock synthesis with a quartz VCO (VCXO) [191]. Another approach is to distribute 

the clock through a serial data link. Howerver, without a low jitter reference clock, a high quality clock, 

for instance from a LC-VCO is necessary. Design of low-noise VCOs and high performance clock 

recovery circuits in CMOS technology are an ongoing challenge for research. The next R&D phase will 

determine what clock recovery performance is achievable in a real system environment, what architecture 

is used best (VXCO, LC-VCO) and whether or not it is good enoug as timing reference for the time of 

flight detector. 

9.7.2 SerDes and Optical driver 

Up to now a high speed optical transmission system consists of many different components, which are 

assembled in a multi-chip-module (MCM) or on a printed circuit board (PCB). At the moment two components 

must be optimized for their function and therefore cannot be included in the standard CMOS 

process. They perform the electrical/optical conversion, and are typically implemented as the light emitting 

device (VCSEL diode) and the light receiver (PIN diode). 

The goal is the integration of all other circuit parts of the optical communication system into the standard 

CMOS 0.18 µm technology. If the designs are modularized it will be useful as IP cells in many different 

configurations or as complete high-speed serial communication device (refer to section 9.8.2). 

parallel 

interface 

input 

parallel 

interface 

output 

8B/10B 

encoder 

control & 

debug 

interface 

8B/10B 

decoder 

serilaizer 

control & 

status 

registers 

deserilaizer 

preequalization 

clock & data 

recovery 

laser driver 

with PhDet 

laser driver 

CML 

Output 

CML 

input 

transimpedance 

Amplifier & 

limiting amplifier 

to VCSEL 

on-chip 

to VCSEL 

off-chip 

to optical 

transceiver 

from optical 

transceiver 

from PIN 

diode 

Figure 9.24: Simplified functional 

blocks of a high-speed serial 

communication system 

The functions of the optical communication system as it is implemented in the OASE chip is shown in 

Fig. 9.24. Shaded blocks are assembled by layout synthesizer tools. The rest of the cells are full custom 

design cells. 

9.7.2.1 Modules 

A first high-speed serializer/de-serializer (ser/des) chip (OASE) was developed in the UMC 0.18 µm 

process. In this chip, the ser/des, high data rate transceiver (CML input/output) and optical front-end 

circuit (laser driver and trans-impedance amplifier) are combined in one large full custom macro cell. 

Design flexibility will be gained by seperating this macro cell into smaller modules. The serializer


and de-serializer are core modules with a low data rate interface. Fig. 9.25 depicts three options of 

combining the serializer with a current mode logic (CML) output driver, a VCSEL driver with off-chip 

photo detector, and a VCSEL driver with on-chip photo diode. The de-serializer can be combined with 

a CML input buffer for interfacing to an external transimpedance amplifier and a photo diode, as shown 

in figure 9.26. 

Serializer Options 

1. Serializer with 

CML Output 


VCSEL Driver and 

Off-chip PhDet 


VCSEL Driver and 

on-chip PhDet 

10 

n 

10 

n 

16 

10 

10 

n 

16 

10 

Serializer 

Serializer 

Serializer 

ADC 

2 

CML 

Tx. 

2 

2 2 

LD 

Driver 

ADC 

2 2 

LD 

Driver 

+ 

Onchip 

PhDet 

Figure 9.25: Examples of serializer configurations 

Deserializer Options 

1. Deserializer with 

CML Input 

2. Deserializer with 

Pre-Amplifier and 

Limiting Amplifier 

10 

n 

10 

n 

10 

Deserializer 

Deserializer 

ADC 

2 

CML 

Rx. 

2 

2 2 

Pre-Amp 

+ 

Limiting 

Amp. 

Figure 9.26: Examples of deserializer configurations 

Reasonable integration of the high data rate cells cannot be done by automatic placing/routing tools. 

Connections between them are critical with respect to performance. Therefore an interactive method is 

required, i.e. the high data rate IP cells have to be placed manually in order to get short connections. The 

low data rate interface ports of the cells can still be connected by layout synthesizer tools. Examples are 

given in Fig. 9.27 and Fig. 9.28. 

Serial Out Serial Out 

Vdd/Gnd 

Layout of Serializer 


Ctrl. Signal 

Vdd/Gnd 

Layout of 

CML Tx. 

Serial Out VCSEL 

Drive Out 

Vdd/Gnd 

Vdd/Gnd 

Layout of 

VCSEL Driver 

Ctrl. Signal 

Figure 9.27: Examples of “construction by 

placing”: layout of serializer with 2 different 

output options, implementing a CML and a VC- 

SEL driver 


Ctrl. Signal 

Serial Out Serial Out 

Vdd/Gnd 

Layout of Deserializer 

Vdd/Gnd 

Serial in 

CML on-chip Driver 

Vdd/Gnd 

Layout of 

CML Tx. 

Layout of 

CML Rx. 

Vdd/Gnd 

Serial In 

CML Data Selector 

Filled Cell 

For loop-back options 

Figure 9.28: Examples of “construction by 

placing”: layout of a ser/des with a CML Rx/Tx 

and loop-back functionality 

Every IP cell requires specified positions of connection ports, for instance the serializer data output port 

directly connects to serial data input port of the CML output driver. Optional IP cells such as VCSEL 

output driver have the serial data input port at the same position. I/O cells, like CML output driver, have 

to bypass the power and ground connections to the inner cells. An example of a ser/des with optional 

loop-back functionality is shown in Fig. 9.28. Filler cells are required here to connect the internal loop 

back signals. There are also filler cells with on-chip CML drivers required for the additional loads of 

on-chip connections. CML data selectors will be used in the filler cell of the deserializer.


9.7.2.2 SerDes Circuit and System Design Consideration 

An important factor in SerDes design is the quality of the synthesized high frequency clock. The choice 

of the MUX structure in the serializer is depending on the available quality of the high frequency clock. 

If on-chip inductors are available, LC-VCO based oscillators can be used and the CMOS serializers can 

run at full data rate as shown in Fig. 9.29. A clock multiplier unit (CMU) based on a multi-phase ring 

oscillator needs another approach, like the one-forth data-rate multi-phase MUX, depicted in Fig. 9.30. 

G 

G 

G 

G 

08; 

08; 

)UHT 'LYLGHU 

%\ 

VHO D 

08; 

)UHT 'LYLGHU 

%\ 

G 

G 

/DWFK 

6HULDO 'DWD 

)XOO UDWH 

FORFN 

VHO D 

)XOO UDWH 

FORFN 

/DWFK 

/DWFK 

&RUH 

08; 

VHO D 

08; 2XW 

Figure 9.29: Full-rate clock 

multiplexer with tree structure 

Another possible solution for a ring-oscillator-based VCO is a dual-loop system as shown in Fig. 9.31 (top). 

The first loop is for filtering jitter of the reference clock. Loop bandwidth affecting the jitter transfer function 

has to be low. The improved quality clock is utilized as reference clock for the second loop, that 

generates the multiplied clock. Bandwidth of the second loop has to be as high as possible to track the 

improved reference clock. In conclusion, the dual-loop structure utilizes the first loop as jitter filter and 

the second loop as frequency multiplier. Fig. 9.31 (bottom) depicts a multi-phase PLL combined with 

multi-phase MUX. 

G 

G 

G 

G 

G 

G 

G 

G 

FON 

FON 

FON 

FON 

FON 

FON 

FON 

FON 

FON 

FON 

FON 

FON 

FON 

FON 

FON 

FON 

&ON 

&ON 

&ON 

&ON 

6HULDO 'DWD 

Figure 9.30: One-forth-rate clock multiplexer 

[188] 

9.7.2.3 Clock Multiplier Unit (CMU) 

5HI FORFN 

5HI FORFN 

3)' /) &3 9&2 

3// ZLWK ORZ ORRS %: )UHT [ 1 

D 'XDO /RRS 3// 

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3KDVH 

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&RQWURO 

9&2 

0XOWL SKDVH 3// ZLWK ORZ ORRS %: 

)UHT [ «1 

3)' /) &3 9&2 

) GLYLGHU 

%\ 1 

3// ZLWK ZLGH ORRS %: )UHT [ 1 

2QH 1 WK 

PXOWL SKDVH 08; 

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6HULDO 'DWD 2XW 

+LJK IUHT 

2XWSXW FORFN 

Figure 9.31: Dual loop PLL (top) and multiphase 

serializer system (bottom) 

A PLL-based clock multiplier unit was implemented. It has a fully differential structure to reduce the 

sensitivity to power and ground noise. Measurement results are depicted in Fig. 9.32. Supply voltage 

in measurements is 1.8 V. The CMU can generate a 5-times clock output and has a jitter of 7 ps (RMS)


at 1.25 GHz. It is used for multiplexing parallel data to 2.5 Gbps serial. The CMU module consumes 

16 mA and its size is 0.47 × 0.43 mm 2 . 

Figure 9.32: CMU measurement result: output clock, jitter histogram at 1.25 GHz (left); output clock in time 

domain (right) 

9.7.2.4 Multiplexer (MUX) 

The multiplexer is based on a half-rate CML tree structure at the front-end and CMOS MUX at slow data 

rate. It has been implemented together with the CMU as a complete serializer. Fig. 9.33 (left) shows a 

die photograph of the complete serializer including VCSEL driver. 

Figure 9.33: Die photograph of the complete serializer including VCSEL driver (left) and the layout of the entire 

high-speed ser/des chip OASE3 (right) 

The serializer was tested successfully up to 1.5 Gbps, as shown in Fig. 9.34. Area is 0.35 × 0.45 mm 2 

and current consumption 20 mA. Supply voltage in measurements is 1.8 V. 

In order to reach the 2.5 Gbps specification, the circuitry was revised and improved in the latest version of 

the chip (OASE3). The loop bandwidth of the CMU was reduced to filter jitter from the reference clock. 

The voltage swing of the CML components was increased to improve matching properties. All clock 

buffers were redesigned to guarantee rise/fall times. The power consumption is optimized by reducing 

CML front-end parts. The IC is still in manufacturing by UMC, expecting first silicon early 2005. The 

layout of the entire chip is shown in Fig. 9.33 (right). 

9.7.2.5 VCSEL Driver 

Front-end circuits for driving VCSEL have been designed and implemented. Design features are a current 

balance concept to reduce power and ground noise, pre-emphasis to compensate high frequency losses. 

The VCSEL driver has been measured with an equivalent VCSEL load as shown in Fig. 9.35. The 

driver is suited for 2.5 Gbps. The output eye-diagram shows 28 ps (p-p) jitter without pre-emphasis 

(Fig. 9.35 (left)). The jitter reduces to 20 ps (p-p) with pre-emphasis (Fig. 9.35 (right)). The VCSEL


Figure 9.34: Serializer output at 1.5 Gbps, constant pattern (left); eye-diagram of serializer output, random 

pattern (right) 

Figure 9.35: VCSEL driver output: Eye-diagram at 2.5 Gbps without pre-emphasis (left); Eye-diagram at 

2.5 Gbps with pre-emphasis (right) 

driver consumes 2.8 to 4.1 mA of the 3.3 V supply voltage and 6.6 to 7.6 mA of the 1.8 V supply voltage. 

The area of the module is 0.29 × 0.23 mm 2 . 

9.7.2.6 Transimpedance Amplifier (TIA) and Limiting Amplifier (LA) 

The design contains as first stage single-ended transimpedance amplifier, then a single-ended to differential 

converter and finally a CML-based limiting amplifier. All modules are implemented and measured 

with an equivalent photodiode load. TIA and LA consume 7 mA and can operate at 1 Gbps. The die area 

is 0.10 × 0.15 mm 2 . The output signal is connected through CML buffers and long on-chip connections 

accross the testchip. CML buffers were redesigned and on-chip connections are optimized in the latest 

chip generation. 

9.7.2.7 DLL-based Clock Data Recovery (CDR) 

The DLL-based CDR has two loops in the system. The first loop is a PLL loop that generates four-phase 

half-rate clocks from the local reference clock. In the second loop, implemented as DLL, the phase of 

the serial input data is adjusted for optimal position of the half-rate clock. Fig. 9.36 shows measurement 

results of the DLL-based CDR. The supply current is 42 mA, the die area is 0.65 × 0.66 mm 2 . Supply 

voltage in measurements is 1.8 V. 

The DLL-based CDR can be applied to systems that have a clock distribution. A more universal alternative, 

the frequency-phase-locked CDR was implemented in the latest OASE chip.


9.7.3 Low cost electrical / optical conversion 

Figure 9.36: Clock at 625 MHz for 

1.25 Gbps (top left); serial data input 

at 1.25 Gbps, DLL-loop disable 

(top right); serial data input, DLLloop 

enable (bottom left); final phase 

position of Serial data input (bottom 

right) 

In the future a key technology for high speed interconnects will be the optical transmission. In order to 

keep cost low an optimized system will be designed and tested which can cover distances from centimeters 

up to 100 m. 

The OASE project of the ASIC-CC in cooperation with the Optoelectronics Group of the University 

of Mannheim provides the basics for such a low cost interconnect technique. The optical interconnect 

between the VCSEL and the fiber is realized by a new self-aligning fiber attachment structure which 

contains also a 45 degree mirror prism to fed the laser light into the fiber. 

fibre/mirror 

glas substrate 

GaAs-VCSEL 

chip substrate 

Figure 9.37: Principle of optical interconnects using 45 ◦ mirrors 

9.7.3.1 Development of 45 ◦ microprism in SU-8 photoresist for optical interconnects 

Exchanging data off-chip at high speed is becoming a bottleneck in high performance electronic processing 

systems. As data rates increase, electrical interconnects are limited by power, distortion, cross-talk 

and pin-out capacity. On PCB’s, the dielectric losses and the losses due to skin effect significantly rise 

above 1 GHz. Optical interconnects do not show these limitation and therefore electrical interconnects 

will be likely replaced in the near future. VCSELs (vertical surface emitting laser) reach more than 

10 Gbits/s and with this new developed system, directly coupling the emitted light into a fibre, interconnects 

may become possible at very low cost. The idea is to combine a VCSEL array with an microprism 

array in one alignment step for a whole wafer (Fig. 9.37). 

SU-8 microprisms accomplish two tasks in one. They act as a mirror, reflecting the light by 90 ◦ and 

reduce the complexity to align a fibre to the VCSEL. For this reason, a process was developed to create 

alignment and deflection SU-8 structures with UV-illumination. The challenges for producing non 

vertical SU-8 structures are: 

• Limitation of exposure angle due to refraction;

9.8. FEE Communication 221 

• Minimize reflections from index boundaries; 

• Optimization of process parameters such as exposure time; 

• Increasing the quality of structures. 

Figure 9.38: Examples of small outline low-cost optical 

connectors, suitable for the required optical connectivity of 

the OASE architecture. It allows in particular to connect to 

the pig-tail type fiber connections of the OASE module. 

250µm 

Figure 9.39: Microprismarray in SU-8, top view (left), 3 Laser light coupled into a fibre and reflected at the 

microprism (right) 

By index matching with water between every critical layer, and “planar-fitting” the mask it was possible 

to produce microprisms in SU-8 (Fig. 9.39). Mass production with injection casting in the future can 

significantly reduce the cost for each structure. 

9.8 FEE Communication 

The existence of some 10k front-end chips or modules on the detectors pose the requirement for the 

distribution of synchronized clocks, global time or reset as well as the readout of these modules at the 

defined rate. Finally there is a corresponding requirement for initialization and configuration of these 

distributed modules. All those requirements share the necessity of peer to peer communication as well 

as the precision recovery of clock signals. This section is dedicated to the appropriate issues. 

9.8.1 Clock/Time Distribution 

There are several thousand front-end devices in the experiment, which all require to have the same time 

at the granularity of about 1 ns and have to have a synchronized clock with a maximum jitter of 100 ps, 

if the time of flight system is excluded in this context. If the ToF system is included the maximum 

clock jitter is limited to 10 ps. The LHC experiments use a passive optical fan-out of a reference clock 

master [192]. 

It should be noted that there are going to be a large number of high-speed multi gigabit links, used 

for the readout of the front-end digitizers. Each multi gigabit receiver has to implement a PLL for the


Data 

clk 

Ctl 

clr 

n 

Serializer 

t 0gen 

2 

∆t = const ±δ ≤0.8ns 

2 

Deserializer 

t 0dec 

n 

Data 

clk 

Ctl 

clr 

Figure 9.40: Building blocks 

for clock and time synchronization 

and distribution 

clock recovery with a maximum jitter, being much better than the bit width, and which is synchronized 

to the appropriate PLL of the corresponding transmitter (refer also to section 9.7.1). For instance a 

canonical 2.5 GBit link has 400 ps per bit, basically requiring a receiver clock stability, which matches 

the requirements, stated above, excluding ToF. Note further that the clock stability requirements of a 

12-Bit, 40 MSPS ADC (refer to section 9.6) is compatiple with the requirements of the ToF system. 

Most electrical/optical converters, as well as serializer/deserializer chips are built in full duplex. Therefore 

the implementation of an appropriate uplink does not result in significant extra cost. This link could 

be used also for the distribution of configuration and maintenance data (DCS). Using such multi gigabit 

uplink the front-end receiver PLL is synchronized with the backend TX clock, while providing an 

acceptable clock jitter. This observation drives a new concept, where the clock distribution and synchronization 

between the front-end and the back end is performed by just using the available multi gigabit 

transceivers. The uplink clock fan-out can be done both electrically or also using some passive optical 

splitters. In case of an electrical fan-out some redundancy has to be implemented in order to avoid single 

points of failure. One area of required research is the number of PLL hierarchies, which can be achieved 

with the given clock jitter requirements, which depends on the quality of the receiver PLL. Appropriate 

test chips are planned to be designed and qualified in this context (refer also to section 9.7.1). Note that 

an electrical fan-out of 4, which is easily achieved, and a hierarchy of 5 layers results in a fan-out of 

1:1000. The GSI accelerator is currently developing a so-called bunchphase timing system [193] which 

will deliver a highly stable clock anywhere on the campus with a maximum jitter and long term drift of 

100 ps. This infrastructure forms an ideal distributed root of the discussed trigger/clock fan-out. 

Another requirement is the distribution of a global time, which reduces to the counting of the synchronized 

clocks and the distribution of a synchronized clear signal to all counters. Such clear signal has 

to be distributed with a maximum jitter, less than the time clock period (1 ns). This can be also easily 

implemented in the appropriate serializer/deserializer as special purpose packet, which is forwarded at 

high priority without any queuing delays. 

Fig. 9.40 sketches an appropriate configuration. The serializer/deserializer has a parallel data input with 

appropriate clock and enable or control inputs. In addition a clear input/output signal is provided, which 

triggers the transmission of an appropriate control message with minimal distribution jitter. Note the 

transmission latency is entirely irrelevant here since this is a calibration parameter. 

9.8.2 Generic Optical Advanced Serializer/Deserializer (OASE) 

In the context of high speed data transmission, clock recovery and coherent time distribution a few additional 

features are usefull to add to the functionality of the OASE (optical advanced serializer/deserializer). 

It is planned to develop this device as generic chip and as appropriate designware building blocks, allowing 

their integration on specific front-end chips, implementing clock/time synchronization and data 

readout. The overall OASE building blocks are sketched in Fig. 9.41 and discussed below. In general this 

device implements all required functionality to form a multi gigabit serializer/deserializer like a copper 

substitute on one hand but also allowing to use fractional bandwidths for house keeping functionality, 

such as DCS. In addition it implements a rich set of functionality, typically required in the context of 

front-end DCS, therefore minimizing the amount of glue logic at the front-end level.

9.8. FEE Communication 223 

IO 

JTAG 

I2CM 

I2CS 

Vout 

Ain_A 

Ain_B 

Temp. 

Sens. 

SPI, 

PIO 

250 MBps 

Ser_in_S 

REF_clk_in 

clear_in 

2.5 GBps 

Ser_in_A 

Ser_in_B 

OPTICAL 

CML_A 

CML_B 

CML_C 

CML_D 

CML_E 

PGA 

MUX 

Slow 

ADC 

DAC 

JTAG 

master 

Activity check 

auto select 

Deserializer 

CDR 

Serializer 

Separate drivers 

with progr. 

enable 

DMEM 

+ HM 

I2C 

master 

FIFO buf rec 

clear 

REF clk 

CPU core 

out 

A 

B 

A/B 

M2 

A 

B 

A/B 

M5 

out 

out 

IMEM 

+ HM 

M1 

requests to the arbiter 

ROM 

State machine 

send - 

receive 

A/B 

A 

B 

BYPASS\ 

BYPASS\ 

M3 

A 

B 

A/B 

out 

M4 

out 

I2C 

slave 

A/B 

A 

B 

FIFO 

send 

IO Bus 

ARBITER 

FIFO buf send 

clear 

REF clk 

SDR 

16 bit 

125 MHz 

Masters on the internal bus, 

connected to the Arbiter 

(like in trap chip) 

The 8-th link is redundant 

uplink, to be connected 

to Ser_in_S, REF_clk_in, 

clear_in of the neighbour 

chip 

Clock and clear 

fanout 

SDR 

16 bit 

125 MHz 

Slaves on the internal bus, 

all outputs ORed together 

(like in trap chip) 

PDLY 

PDLY 

DDR 

8 bit 

clear[8..0] 

REF_clk[8..0] 

OUT[8..0] 

LVDS 

DDR 

PDLY 8 bit 

IN[7..0] 

Figure 9.41: Architecture block diagram of a generic multi purpose optical advanced serializer, deserializer 

(OASE) 

9.8.2.1 Fan-Out, Fault Tolerance 

The output of the serializer implements an appropriate CML driver for the optical conversion. It is 

planned to implement several (for instance four) independent CML drivers, all sharing the same input, 

therefore implementing a passive, electrical fan-out. Each CML output can be enabled individually, 

therefore not wasting power for outputs, which are not required. 

The OASE chip is to implement two deserializer inputs, allowing to receive clock and serial data via 

redundant paths. A simple arbiter checks after RESET which channel to use as active input channel 

and reference clock source. Therefore any error in the signal transmission will avoid the loss of the rest 

of the fanned-out tree, avoiding single points of failure. This redundancy can be implemented, at all 

hierarchy levels, for instance, implementing some kind of daisy chain. Each receiver is to implement an 

appropriate programmable delay unit, allowing to adjust the individual phase of the received clock and 

data appropriately. 

In case of the OASE operating in fan-out mode the TX clock is derived from the RX clock. It will be 

enabled only after successful synchronization of the RX PLL. In order to allow injection of the synchronous 

reference clock and time pulses the OASE chips may also be used like physical interface chips 

in the back-end, where the receiver logic is most likely implemented in FPGAs. 

9.8.2.2 DCS Link, Bandwidth Segmentation 

The original motivation for the high-speed transmitter is the downstream data readout and the motivation 

for the uplink is the clock and time distribution. However given the availability of these high-speed 

links it is also possible to absorb the entire DCS network functionality here. But it should be noted that 

the readout and DCS paths have to be absolutely independent, in order to guarantee that no data flow 

scenario may affect any DCS message transfer. The logical independence of data readout and the DCS 

CTRL 

CTRL


messaging is an essential requirement. The fact that the same physical media is considered for the use of 

both data paths is no contradiction, provided that the appropriate guarantee of service on the network is 

given and the two functionalities readout and DCS are implemented entirely independent. 

The requirements stated above are easily provided, for instance by reserving a certain fraction of the 

bandwidth of the serial link for DCS, using some fixed time multiplexing, thereby also guaranteeing a 

defined latency. For instance 1% of the bandwidth of a 2.5 GBit link corresponds to 25 MBits of an 

individual DCS path, far exceeding all known field buses and certainly being more than adequate. 

The uplinks are fanned out in a passive fashion, thereby logically implementing a broadcast architecture. 

However, a fan-out of 1:100 will provide the same individual bandwidth like above. The receivers have 

to implement appropriate logic to intercept only packets, destined to the appropriate node. It is also 

easily possible to implement reliable broadcasts here. A similar broadcast scheme was used in case of 

the LHC TTC system [192]. 

Another important aspect to be considered here is the segmentation of the available bandwidth onto several 

front-end chips. This feature is useful because the required readout speed of one front-end chip will 

be much lower than 2.5 GBits and also there may be several front-end chips on one readout board. In 

that case it is favorable to run the serial output of the front-end chips at lower bit rates, running at appropriately 

reduced power, and merging them by one OASE per readout board, which is also responsible 

for the generation of the clock and timing signals. The same functionality may be used to merge the 

fractional DCS downstream links in the back-end systems. 

The inputs and outputs of the OASE chip operate at 120 MHz DDR, using LVDS as physical transport. 

These transfer rates have proven to work well over distances of up to a few meters, including connectors, 

even in less than perfect environments, where the impedance and damping can only be controlled within 

limits, such as low-cost PCBs. The parallel inputs and outputs can be interpreted as independent serial 

links, running at 240 MBits/sec, with respect to the synchronous reference clock, therefore allowing 

each front-end chip to use fractional bandwidth in multiples of 10% of the OASE link bandwidth by 

implementing an appropriate number of LVDS cells. This architecture generates a power requirement of 

about 15 mW per 240 MBit/s link. 

The OASE chip can also be configured to interpret its LVDS inputs and outputs as a DDR word stream, 

allowing its application as generic, full duplex electrical/optical network physical converter. 

9.8.2.3 DCS functionality 

Given the availability of a full duplex DCS link into the OASE chip it is a natural consequence to implement 

also the functionality, typically required here. Again the silicon real estate will not become a 

determining cost factor. A variety of functions are integrated into the DCS functional subblock of the 

OASE chip. It has to be stressed again here that this block is to be designed entirely independent of 

the rest of the chip, operating on the defined fractional, guaranteed bandwidth of the high-speed links. 

In order to keep this functionality as flexible as possible it is to implement a generic microprocessor 

with built-in instruction and data memory. The instruction memory can boot from either a local serial 

FLASH or via the optical link. The microprocessor is to perform all required independent house keeping 

functionality. 

The DCS functional block can also include a programmable instrumentation amplifier/attenuator with a 

10-Bit sampling ADC, temperature sensor, reference voltage generator core and I/O voltage sensors and 

the like. Such an analog building block was also integrated into the ALICE TRD TRAP chip (refer also 

to sections 9.2.3, 9.2.4). 

It also implements the necessary interfaces to the front-end, such as JTAG, I2C, SCSN, SPI and the like.

9.9. CNet – Concentrator Network 225 

Since it is unlikely that all of them are used simultaneously some pin sharing may be implemented here. 

In addition some parallel input/output signals will be useful. All the discussed interfaces can be made 

available to the built-in microprocessor or directly to the DCS network for direct remote access. 

This functionality allows to fully and autonomously initialize and control all other chips on the readout 

board by the OASE. It can even do so without appropriate up-link by providing a quartz oscillator, which 

may be used in case of no operational uplink. The configuration data can be stored in the external serial 

FLASH, which can be reprogrammed in situ via JTAG and control of the DCS network. 

This DCS functionality eliminates most of the typically required hardware on the front-end modules, 

which is related to house keeping and safety monitoring. A group of such front-end modules is expected 

to be then monitored by an embedded DCS front-end computer, such as [194]. 

9.8.2.4 FPGA reconfiguration 

SRAM based FPGAs have the problem of possible radiation induced loss of configuration during the normal 

experiment operation. This problem can be mitigated by implementing in-situ reconfiguration and 

verification of the FPGAs configuration. Such functionality is currently being implemented in ALICE 

and was successfully demonstrated in various test beam experiments. It is implemented best, using some 

configuration master, which itself is radiation tolerant, such as the OASE chip. Since it is an ASIC it is 

possible to implement all necessary functionality to detect and correct for single event upsets. 

The built-in microprocessor is available to operate the required reconfiguration and verification functionality, 

using a defined interface, such as the XILINX ICAP. The configuration can be taken from the 

external FLASH or the optical link, should specific parts of the design be monitored. It can also be used 

for health and sanity monitoring of the FPGA operating in situ. However, typical FPGA configuration 

data sets can reach the size of megabytes, being segmented into individual blocks. In order to avoid on 

one hand long verification times by reading the configuration from a serial FLASH and on the other hand 

the need for a high speed parallel FLASH interface, requiring a large number of I/O signals, a checksum 

of the individual FPGA configuration blocks can be computed and stored in the small internal OASE 

data memory. Then only in case of a configuration error the appropriate FPGA configuration block is 

reread from the external serial Flash and programmed into the FPGA automatically. 

9.9 CNet – Concentrator Network 

9.9.1 General Requirements 

There is no global trigger for the read-out of data, which means that the detectors are free running data 

acquisition systems. Each channel has local trigger units which identify individual hits locally. The 

local threshold values should be as near as possible to the noise level to capture any significant pulse. 

In addition it is typically required to readout pre- and post history and also geographically neighboring 

channels, possibly being below their individual hit detection threshold, in order to capture the entire hit 

cluster (refer also to section 9.3). Therefore it is necessary for each channel to be able to communicate 

to its geographically neighboring channels, that they are to reaout a certain group of time bins independent 

of their individual hit detection status. Since most geographically neighboring channels are also 

located on the same readout modules this can be done by direct signalling between the appropriate frontend 

chips. However, there are always boundaries and areas where such next neighbor signalling is not 

possible. For those cases the bidirectional CNET functionality can be used. However, this also puts a 

relatively high demand onto the maximum tolerable message passing latency on CNet, since the readout 

message has to be received within the number of available time samples stored in the short input analog


memories (refer to section 9.5). 

Another general requirement is the implementation of the coalescing of the data stream and the associated 

hit clusters to event clusters, using the available time information. This feature allows to increase the data 

payload of the readout system downstream. 

The bidirectional CNet system supports also the distribution of clock and time information by using the 

otherwise idle uplinks. DCS messaging is implemented using a reserved fraction of the available full 

duplex bandwidth. 

9.9.2 CNet System Level 

The main function of the CNet is the data read-out from the FEE units, coalescing of hits, belonging to 

the same event or time interval and finally the transport of the data to the BNet interface. Thus the nature 

of the CNet itself is unidirectional. However, given the arguments outlined above, additionally the global 

clock and time information (TNet) and the DCS function are mapped to the CNet. Advantages from this 

are reduced costs and higher flexibility. Therefore the CNet links are bidirectional 

Given the availability of a very low-cost electrical/optical conversion (refer to section 9.8.2), only onboard 

and very local connectivity is expected to be implemented in copper. The remaining connectivity 

shall use multi gigabit glas connections. Additional advantages of optical interconnects are: scalable data 

rates, medium distances can be bridged up to 100 m (compared to very short electrical distances, less 

than 1 m). More benefits of optical interconnects are the electrical isolation of components, no electrical 

interference and low material budget. 

The CNet system level architecture is sketched in figure 9.42. The front-end (left) is typically implemented 

on some readout boards, implementing several front-end digitization and hit detection chips 

(FEE). In a typical case the required down stream bandwidth is signifficant below a GBit, if derandomized 

appropriately by implementing appropriate small buffers. A group of front-end chips is connected 

to an OASE serializer, which also generates all required low jitter reference clocks and fans out the appropriate 

DCS messages. 120 MHz DDR LVDS links are foreseen here. The output of the OASE can 

be implemented redundant by using two of the CML output drivers. Groups of individual OASE chips 

in the front-end are also connected in form of a daisy chain, in order to have a redundant input for the 

reference clock and maintenance information. 

Note that the number of FEE chips per front-end module is not only determined by bandwidth requirements. 

The availability of very low-cost optical converter modules, such as OASE, may drive the size of 

these front-end modules to become rather fine grain with a small number of front-end chips per OASE 

module, while underutilizing the available link bandwidth. Some advantages of this fine grain approach 

are economy of scale due to higher volume of simpler modules, such as reduced cost per module and increased 

yield. The CNet functionality of the OASE concentrators avoids the direct interconnects between 

FEE chips by implementing the required readout trigger switching. 

A large number of CNet readout down stream links will be required by the experiment. The physical 

layer and protocol will be designed such, that they can also interface to generic ser/des chips, in particular 

including FGPAs. To date there are appropriate FPGAs available implementing up to 24 multi gigabit full 

duplex links. All switching and coalescing or data compression functionality can be implemented very 

flexible here. Given a fan-in of 24, 50 CNet reciever modules are required, implementing one merger 

FPGA each, in order to serve 1000 CNet optical links. This number of units is easily implemented in two 

6U crates. A similar architecture was chosen for the ALICE GTU, implementing 1080 2.5 GBit links. 

The merger FPGA also implements connectivity to its next neighbors in order to allow the next neighbor 

distribution of hit information to trigger the readout of other front-end chips.


Analog 

inputs 

Analog 

inputs 

Analog 

inputs 

Analog 

inputs 

FEE 

. . . 

FEE 

FEE 

. . . 

FEE 

Clock, T0, 

RTrgX, DCS 

DATA, 

RTrgX, DCS 

. . . 

. . . 

Copper 

250 Mbps 

OASE 

OASE 

Optical 

2.5 Gbps 

redundant downstream link 

downstream link 

(DATA, RTrgX, DCS) 

Upstream link 

(Clock, T0, RTrgX, DCS) 

optical 

SO - SOASE 

OASE 

OASE 

OASE 

OASE 

OASE 

Merger FPGA 

DATA 

RTrgX 

CNET RTrgX next neighbors 

OASE 

DCS frames 

DCS 

SBC 

BNet 

readout data 

Ref. clk / t0 

Ethernet 100MBps 

configuration data 

Figure 9.42: Architectural building blocks of the CNet front-end (left) and its back-end, interfacing to the BNet 

infrastructure 

Figure 9.42 (left) sketches the appropriate architecture. The high-speed down-stream links feed directly 

into one large FPGA module. The upstream signalling does not require a high individual bandwidth 

and therefore is implemented as passive or active broad cast network. The figure sketches an electrical 

fan-out. 

Slow control messaging is orchestrated by a DCS single board computer. It is expected to implement 

a low cost single board computer, running Linux here. The back-end network is Ethernet here (refer 

also to section 10.5.4.5. DCS messages generated in the front-end are stripped by the merger FPGA and 

forwarded directly to the DCS single board computer for further processing. DCS messages to be sent 

to the front-end are generated on the DCS single board computer and are forwarded to the OASE root 

module for further fanning out. Given a DCS messaging band width of 25 Mb and assuming a link bandwidth 

of 2.5 Gb/s a fan-out of 25 will result in a 25% utilization of the available link bandwidth, allowing 

enough available bandwidth for the up-stream distribution of geographic neighbor readout commands to 

the appropriate front-end chips. The messages are stripped by the appropriate OASE modules via their 

individual geographic ID, which is defined by the location of the reaodut-board. The appropriate OASE 

root module also receives a reference clock and t0 signal for clearing all time counters. These signals are 

expected to be derived from the GSI BuTis system [193]. 

The CNet readout, control and clock synchronization network is implemented as cost-effective, hierarchical, 

redundant, optical interconnect structure. Since the time and clock distribution functionality 

(TNet) is also included in the CNet functionality, no clock recovery is the required in the FEE chips. The 

result is a synchronous FEE/CNet Subsystem (refer also to figure 9.42 (left)). Additionally, effort may 

be put into the development of an extremely low-jitter PLL element, in oder to reach the strict timing 

requirements for the Time-of-Flight detector. 

The CNet over all tasks are summarized in the following list: 

1. Transport of data from FEE units on the detector via the CNet to the BNet interface in the counting 

room. 

2. Combining of correlated hits to hit clusters, based on the hit time information wherever possible 

in order to reduce the messaging rate and overhead.


3. Independent use of fractional bandwidth for bidirectional communication of DCS control- and 

status messages. 

4. Independent upstream time distribution with time stamps and epoch markers. 

5. Independent upstream synchronous clock distribution. 

6. Nearest neighbor very low latency hit communication to geographically adjacent detector channels 

for readout of geographical hit tails. 

7. Redundancy and avoidance of single points of failure, in particular in the area of clock distribution 

and DCS networking. 

8. Radiation tolerance of all network components. 

The distribution of data within BNet will require some flow control and traffic shaping to be implemented 

in order to avoid overutilizing target resources [195]. 

9.9.3 Link Protocol Physical Layer (FELA) 

Given all considerations outlined in the sections above, the CNet physical layer has to support five different 

functionalities. They are supported by a set of different special purpose messages, which are 

summarized in table 9.3 and will be discussed in detail in the following. The overall principle is based 

on the minimization of the overhead for message headers on one hand and to keep the messages as 

short as possible in order to achieve a very low latency for the transmission of some of the hit trigger 

messages. Both serial and 8-bit double data rate physical transmission paths have to be supported. The 

required logic for message processing has to work in FPGAs as well, limiting the internal clock rate. 

Therefore a 16 bit data path was assumed for all chip or FPGA internal processing requiring a word 

clock of 120 MHz for the 2.5 GBit transmission lines. Therefore all CNet messages have a granularity 

of 16 bits. 

Acronym Prio. Length Function description 

DATA 5 32...272 data readout downstream payload data format detector specific 

DCS 3 32...272 DCS DCS Payload, stripped by merger FPGA 

RdTrgV 4 32...64 hit trigger next neighbor readout trigger command 

RdTrgC 4 16 hit trigger downstream next neighbor readout trigger command 

RdTrgB 4 16 hit trigger upstream next neighbor readout trigger broadcast command 

T0 2 16 TNet upstream time counter reset 

RESET 1 16 DCS RESET command 

IDLE x 16 CNet idle message 

StdBy N/A 16 CNet link powered down message 

Table 9.3: The various CNet frame types and their functions. The frame length includes the 16-bit header/trailor. 

9.9.3.1 Data readout 

The readout of all data, acquired by the various front-end modules, is the primary CNet function. It 

produces more than 95% of the downstream data traffic. The data readout is a unidirectional data path 

flowing downstream from the front-end towards the back-end. In order to keep the maximum latency


small, the maximum frame size is limited. A payload of 32 bytes, constituting an overhead of 6.25% 

for the CNet headers, corresponds to a frame length of 136 ns for a 2.5 Gbit link, well below the stated 

maximum CNet latency. The payload format is detector dependent and entirely transparent to the CNet 

switching functionality. The DATA packets are transparently passed through. They have the lowest 

priority, compared to all other CNet messages. Data messages are variable length with the frame length 

being encoded in the CNet 16-bit header. 

9.9.3.2 DCS 

The available full duplex connectivity provided by CNet can be used also to implement DCS message 

passing. It is emphasized here, that this functionality is entirely independent of all other CNet messages. 

The DCS and other packets only share the same physical transport as many other networks impelement 

several independent data paths, while guaranteeing appropriate quality of service levels. This functionality 

is implemented in the CNet physical interfaces, guaranteeing a minimum bandwidth and maximum 

latency to the DCS data paths, which is quite small given the limited frame sizes. DCS has the highest 

network priority, except for the T0 and RESET functionality, which is executed under DCS control and 

in a way can be considered as DCS subfunctionality. Like data readout DCS frames are variable length 

with a maximum payload of 32 bytes. DCS frames are distributed both downstream and upstream. In 

the first case they share the bandwidth with the data readout, with a minimum bandwidth guaranteed to 

DCS. Given the CNet topology, upstream DCS messages are broadcast to all front-end modules, therefore 

requiring proper decoding. Therefore the DCS payload has to include a front-end target ID. All 

other messages are discarded like in any other broad cast type network. This decoding and corresponding 

encoding of the responses can be easily implemented by the integrated microcontroller of the OASE 

chips (refer to section 9.8.2). 

9.9.3.3 Readout Trigger 

In order to inform FEE modules about neighboring hits and to trigger the readout of geometric neighbors 

the information of a hit has to be distributed. This information has to include the time of hit, and an 

appropriate channel identifier, which can be defined as FEE channel number, FEE ID and the detector 

ID. Not all those bits are required at all levels to be present as they may be defined implicitly. Table 9.4 

gives an outline of the number of bits required for an appropriate encoding. The hit information has to be 

distributed with minimum latency to the appropriate front-end chips, which then readout a preconfigured 

number of time bins and channels around the hit time and coordinate. 

Object Bits description 

Channel 5 assuming a maximum of 32 channels per front-end chip 

FEE ID 17 assuming less than 127k front-end chips per detector 

Detector ID 4 CBM defines 5 major detector systems 

Hit Time 11 at a granularity of 1 ns 2 µs maximum CNet latency 

Table 9.4: Required number of bits to encode a hit message 

There are two formats of readout trigger frames defined. A long frame, containing the entire hit information, 

requiring 64 bits or 32 ns to transmit at 2.5 Gbits/sec. It has to be transmitted and routed by 

CNet to the appropriate front-end modules within less than 2 µs. This frame can be used downstream 

and upstream. 

In case of multiple front-end chips feeding one high-speed link (OASE), a reduced frame length can be


used to communicate the hit information upstream (OASE→ FEE chips), in order to save latency since 

these links operate at reduced bit rates (for instance 4 ns/bit). For the downstream (FEE→ OASE chips) 

communication the third hit trigger frame can be used (RdTrgC). It does not require any payload, except 

for the FEE channel ID. For FEE modules with more than 16 channels, two or more channels may have to 

be grouped together for the purpose of transmitting readout triggers or a longer format has to be defined 

(RdTrgV). The time and FEE ID is added by the OASE chip, implementing a configurable preloaded 

frame, which is sent with the appropriate channel ID every time a (RdTrgC) frame is received. It is 

therefore required for this particulare frame, that there is a fixed latency between the hit time and its 

transmission. 

In order to support synchronized readout of a group of front-end channels independent of their hit information, 

for instance for calibration, base-line readout, etc, the readout trigger broadcast frame (RdTrgB) 

is defined. It has only one parameter, defining the time for the readout window. The pre and post history 

to be used are preconfigured parameters in all FEE modules. This command can be used to stimulate 

software or external hardware readout triggers for an entire region of a detector, by feeding it into the 

upstream broadcast network. A good candidate for the insertion of these frames are the DCS single board 

computers. 

9.9.3.4 Time, TNet 

In order to produce coherent time in all front-end modules, they implement appropriate counters with a 

1 ns granularity. They need to be reset synchronously for proper time synchronization. This funcionality 

is expected to be executed when a run is started, when a synchronization error is detected or at a low 

periodic rate. T0 frames are distributed upstream only. They are single word 16-bit messages, which are 

forwarded by any agent immediately without any time jitter. Therefore they have the highest priority in 

the network. 

The second TNet functionality is the distribution of a synchronized clock to all front-end modules. This 

functionality is provided automatically by the PLLs in the multi gigabit receivers, since they by nature 

have to synchronize to the incoming bit stream with a clock jitter much smaller than the stated detector 

requirement. 

9.9.3.5 Miscellaneous Frames 

There are a few additional single word 16-bit messages, which are in part under the over all DCS control 

and are processed at CNet data link layer, independent of the state of the appropriate receiver. 

RESET frames allows to issue a physical reset to the target device. The second nibble allows to define 

certain RESET or clear types (ClrTyp). The decoding of this frame is implemented directly at the data 

link layer and interfaces directly to the on-chip reset and clear logic, independent of the state of the chip. 

This message allows to RESET a peer module, provided the minimum connectivity is still available. 

IDLE frames are transmitted as keep alive messages to maintan link synchronization. They are not 

required for DC LVDS signalling, which is DC coupled or even powered down. However the reference 

clock has to be distributed then via other means. The unused length bits of the IDLE frame may be used 

to distribute generic system states, etc. 

StdBy frames are defined only for completeness. The LVDS transmitters in the front-end can be disabled 

in order to save power during system idle times. In that case the appropriate receiver will read 

zeros. Therefore the StdBy frame has all bits defined as zero, including the CRC, which is incorrect in 

this case.


9.9.3.6 Physical Format 

The header is composed of four nibbles. The first nibble defines the message type. At this point 9 of 

the 16 possible combinations are defined, leaving enough spare room. The second nibble defines the 

number of 16 bit payload blocks. In order to support a payload of up to 32 bytes, the payload count is 

encoded as N-1. The third nibble implements a 4-bit CRC on the entire packet, including the header. 

It forms the CNet trailer. It should be noted that CRC checking normally requires the implementation 

of a store-forward architecture, since only in that case it is possible to ensure that only packets with a 

correct CRC are being transmitted. However, in order to minimize the message passing latency another 

aproach is implemented. Any message is forwarded as quickly as possible, implementing cut-through 

routing. Should a message be received with a bad CRC the transmitted CRC is being stomped [196]. 

This stomped CRC is defined as the correctly recomputed CRC, being XORed with a stomp value. This 

stomp code to use here will have to be evaluated. This technique allows to identify the first location of the 

error and allows isolate potential hardware problems and does not escalate one error accross the network 

but it also allows cut-through routing of the messages. The final receiver of a corrupted message will 

destroy it as it may not even know whether or not it is the correct receiver, as also address information 

possibly contained in the packet may have been altered. 

(A) 

(B) 

(C) 

SOF XX 

SOF XX 

SOF XX 

SOF XX 

SOF XX 

SOF XX 

0 4 0 15 8 11 

Type Len16-1 Payload Block 1 

… 

CRC4 RTrgV 0010 48 bits (FEE ID 17 – Channel 5 – Hit time 12 – spare 14 ) CRC 4 

SOF XX RTrgC Chnl ID CRC4 EOF XX 

SOF XX RTrgB Tim ID CRC4 EOF XX 

RESET ClrTyp CRC 4 

IDLE 0000 CRC 4 

T0 0000 CRC 4 

StdBy=0 0000 CRC 4 =0 

EOF XX 

EOF XX 

EOF XX 

EOF XX 

EOF XX 

SOF XX DATA NNNN Data header + Data payload 

… 

CRC4 EOF XX 

SOF XX DCS NNNN DCS header + DCS payload 

… 

CRC4 EOF XX 

SOF XX RTrgV 0000 16 bits (Channel5 – Hit time11 ) CRC4 EOF XX 

EOF XX 

Figure 9.43: Outline of the most 

important CNet physical frames, including 

the generic overall format 

(A), frames with data payload (B) 

and command frames without payload 

(C). 

Figure 9.43 shows a physical layout of the various CNet packets. Aside from the three nibbles type, 

length and CRC there are four bits, being used for start of frame (SOF) and end of frame (EOF). The 

particular choice of SOF and EOF coding is a trade-off between latency and power. For instance the 

choice of SOF=001 and EOF=0 gives two leading zeroes before the canonical start bit. Note the LVDS 

receivers will decode a disabled peer transmitter reliably as zero and the time required to safely power 

up an LVDS transmitter is less than 4 ns (refer to section 9.2.2). Two leading zeros in the SOF guarantee 

the start bit 1 to be safely decoded even if the transmitter is enabled only at the moment the SOF is being 

transmitted. Therefore this encoding scheme allows to operate the LVDS links in an on demand fashion, 

requiring less than 7% of the on-line power in stand-by for the LVDS receiver. Note the frame lengths 

sketched in figure 9.43 are not to scale. For completeness a corresponding stand-by packet is defined 

with all bits being zero.


9.10 Miscellaneous, Infrastructure 

9.10.1 Power distribution 

Basically all modern front-end chips require supply voltages below 5 V. For instance the UMC 180 nm 

process operates at a core voltage of 1.8 V and an I/O voltage of 3.3 V. The typical candidates for supply 

voltages to date are 1.8 V, 2.5 V, 3.3 V and 5 V. On the time scale of the CBM experiment it is expected 

not to see 5 V any more and there may be requirements for supply voltages below 1 V. For instance 

FPGAs are candidates here. 

It should be noted also that there are two principal choices, where the first one is distributing some 

higher supply voltage and implementing step down regulators close to the front-end, and the second is 

the generation of the low voltage outside the detector and the distribution of the low voltage into the frontend. 

So far the first approach was typically rejected since the requirements on the step down regulators 

are complex. They have to operate in magnetic fields and their fringe fields, have to be radiation tolerant 

and finally there is always a concern of electronic switching noise and heat dissipation close to the 

detectors. For those reasons also the ALICE TRD and TPC detectors, for instance, chose the second 

model. However, it has to be stressed that in case of ALICE the length of these low voltage distribution 

lines is about 40 m. Further the current requirements add up here exceeding 10 kA distributed over the 

supply voltages 1.8 V and 3.3 V. The voltage drop in the supply lines lies at 1V with an additional 0.3 V 

for the local analog voltage regulation. The added complexity and material budget to distribute tens of 

kiloampere is significant. 

The supply lines in CBM may be shorter as this is not a collider experiment with a solenoidal magnet. 

But also here the length of the low voltage supply lines will easily reach the 10 m level. In order to have a 

more elegant low voltage generation CBM plans to develop a step down regulator with the requirements 

to operate inside the experiment. In this respect CBM follows a standard industry trend. For instance 

the core voltage of the processor in any modern PC is also generated with step down regulators, closely 

located to the current sink. 

The general requirements of the step down regulator to be developed are summarized in the following: 

• use of a low-cost primary power supply outside the detector, for instance generating 48 V DC, 

moderately regulated in order to avoid particular safety and 50 or 600 Hz noise issues of the low 

voltage distribution 

• design the electronics to be radiation tolerant up to 1 MRAD. 

• design of proper inductive transformer to operate in the defined static magnetic fields and fringe 

fields 

• operate step down regulator at as high as possible frequencies (>10 MHz) in order to stay far away 

from typical frequency domains of the preamplifiers. 

• implementation of DCS functionality, allowing to measure voltage and currents delivered by the 

regulator, its core operating temperature and supporting remote control and possibly adjustment of 

the delivered output voltages. 

The regulator is planned to be developed as one integrated control chip, implementing all necessary components 

with a minimum of external devices, such as the transformer, capacities and power components.

9.10. Miscellaneous, Infrastructure 233 

Year Model Complexity 

1990 XC3042 288 LUTs + flip-flops 

1994 XC4005 512 LUTs + flip-flops 

1994 XC40013XL 1,152 LUTs + flip-flops 

2000 XCV300 6,144 LUTs + flip-flops 

2002 XC2V1000 10,240 LUTs + flip-flops 

2004 XC2VP30 27,382 LUTs + flip-flops 

2005 XC4V60-LX 53,248 LUTs + flip-flops 

Table 9.5: Growing of resources for middle-of-the-road FPGAs with same price, equivalent to one day’s engineering 

salary (from [197]) 

9.10.2 FPGA Roadmap 

The complexity of FPGAs and the amount and diversity of ASIC blocks which are integrated together 

with the FPGA matrix is going to increase. Therefore the boandary for what is best implemented in an 

ASIC and what is better implemented in an FPGA is hard to predict at this point in time. At the least there 

has to be a working model as to what is to be expected with respect to FPGA functionality, complexity, 

performance, radiation tolerance and the like. These parameters will have to be monitored during the 

development of the CBM detectors in order to ensure a good choice of FPGA and ASIC combination. 

This section gives a first outlook of FPGA road maps. 

The open question addressed in this section is: How will FPGAs look like in 2010, and what will these 

devices cost? To answer this question two types of FPGAs need to be distinguished: Low-Cost FPGAs 

(e.g. Xilinx Spartan Series, Altera Cyclone Series) and High-End FPGAs (e.g. Xilinx Virtex Series, 

Altera Stratix Series). 

Currently we see improvements into two directions: low-cost FPGAs having the same gate count become 

dramatically cheaper, or we see an enormous gain in terms of logic cells on a chip for the same price. 

High-End FPGAs on the other hand migrate from pure glue logic to configurable system platforms based 

on multiple processing elements, on-chip memory and high speed connection links (LVDS and multi 

gigabit serial transceivers). 

One reason for this trend is that FPGA technology gains the maximum benefit from the development in 

the chip industry. Because of their very regular internal structure FPGAs are, besides RAMs, the first 

chips that can be built in new technologies. General purpose processors are typically following later due 

to their higher complexity. 

In case of low cost Xilinx Spartan FPGAs the cost for 400k gates went from 100$ (1998) to 40$ (2000) 

to 6.5$ (2004). This implies a cost reduction of factor 2.5 every two years. If we extrapolate this curve 

to 2010, we will be able to buy a 400k gate low cost FPGA for a little more than 40 cent. 

One of the major drawbacks of using FPGAs in current applications is the high power consumption per 

gate, compared to standard CMOS ASICs. However, since there is a lot of research going on at the 

moment, we expect a reduction of the power per gate of at least 65% by 2010. Therefore, in eight years 

from now, FPGAs may be used in many cases, where cooling and total power consumption is an issue. 

Watching high-end FPGAs (e.g. Xilinx Virtex), we see a similar development curve. The development 

of the costs in the past show a dramatic decrease, in 1990 one FPGA logic cell costs about 1, in 2003 

this is only 0.002. The resources of middle-of-the road FPGAs with constant price doubles every few 

years, see table 9.5. It can be expected that FPGAs with a capacity well above 100k logic cells and clock 

rates in excess of 500 MHz will become available at commodity prices towards the end of the time frame.


This will allow implementing many complex algorithms in FPGAs. 

Also the maximum clock frequency increases by a factor of 2 every 5 years [197]. Currently the clock 

frequency has reached 500 MHz for FPGA logic. If we extrapolate this development to 2010, we will 

reach 1 GHz as a maximum clock frequency for the FPGA internal logic. Taking into account, that 

FPGAs will contain many (e.g. more than 1000) hard multiply-add cores, FPGA devices become a perfect 

solution for computing intensive DSP applications. 

Additionally to the logical resources, modern FPGAs also offer up to 20 10 Gbit/s serial I/O (SERDES) 

in silicon, together with embedded processors for monitoring, test and control. 10 Gbit/s links over 

optical or copper interconnects as well as the associated switching hardware are expected at affordable 

prices in the next few years. 

It is not sure whether all FPGA system level features, like glitch-free partial reconfiguration, on-chip 

multi-gigabit transceivers, hard processor cores etc. will be available in low-cost FPGAs. However, 

there is a clear indication that at least processor cores will be included to the Xilinx Spartan series very 

soon. 

The above mentioned development is only valid for coarse grain FPGAs like the ones from Xilinx, 

Altera and Actel. However, there are numerous other approaches having more complex programming 

elements as the basic computing elements like the XPP from PACT. The XPP architecture is heavily 

used for implementing communication applications. Further research needs to be done comparing these 

devices with fine grain FPGAs in terms of power effectiveness, compute latency, programming tools and 

radiation hardness. 

As summary FPGAs should be used instead of custom ASICs where ever possible and cost effective. 

The flexibility is an important advantage since design errors may be corrected by simply downloading a 

new configuration file. New algorithms may be deployed in the same manner giving the user a possibility 

to adapt algorithms in a much more convenient way. Many drawbacks like power consumption, slower 

clock frequency and the higher prices for medium and high volumes will mitigate within the following 

6 years. This observation is true in particular, taking into account that the ASIC development cycles are 

much slower, therefore resulting in an older technology being used for ASICs as compared to FPGAs 

(refer also to section 9.1.2). 

9.10.3 Radiation Tolerance 

The rapid development of SRAM-based FPGAs is expected to continue in the coming years. Today’s 

devices are already candidates for time-critical, compute intensive operations at all levels of the readout 

chain. Due to their flexibility, complexity, integrated resources (e.g. DSPs, microprocessors, large 

distributed memory blocks, fast communication cores), computational potential and price, SRAM-based 

FPGAs can outperform general purpose CPUs by orders of magnitude. Although FPGAs are also available 

in flash or anti-fuse technology, the latter are a lot more expensive, much smaller and simpler and 

therefore not an alternative to SRAM-based FPGAs. 

SRAM-based FPGAs have one drawback: they are sensitive to radiation. If they are operated in radiation 

fields, fast hadrons (neutrons, protons, pions, kaons and heavier nuclei with kinetic energies larger than 

20 MeV) traverse the sensitive volume and react with the silicon nuclei. Although the energy loss of 

the projectile in silicon is not sufficient to change the state of a SRAM cell (in today’s technology), the 

spallation products (the recoil nucleus and heavy fragments) deposit larger amounts of energy in the 

sensitive volume. 

Sequential and combinatorial logic, registers and memory blocks can be protected against errors due to 

such bit-flips by classical redundancy methods – data: hamming coding, functionality: triple module

9.10. Miscellaneous, Infrastructure 235 

redundancy and voting scheme. But radiation induced bit-flips – Single Event Upsets (SEUs) – in the 

configuration SRAM are severe. If not corrected, they can cause SEFIs (Single Event Functional Interrupts) 

in the application. Since applications use between 2% and 10% of the routing resources, typically 

only 10% of the SEUs result in SEFIs. SEUs can by monitored by reading back the configuration bit 

stream. Most FPGA devices offer a fast readback for verification and localization of a SEU in the configuration 

SRAM. Once detected and localized, the SEU can be repaired by reconfiguration. Currently 

only Xilinx allows an active error correction via “Active Partial Reconfiguration”. Because most primary 

SEUs are not even visible as SEFIs – and if corrected in a timely manner – secondary effects (SEFIs as 

a combined result of two or more primary SEUs) can be prevented. Almost all errors can be corrected 

this way, maybe resulting in transient upsets (“data noise”), but basically keeping the device alive and 

the application running. 

The applicability of such a correction scheme depends on the rate of SEUs, given as the product of the 

particle flux and the SEU cross section. The cross section per bit in its turn depends on the structure size 

of the technology, e.g. 10 −14 cm 2 for the 90 nm technology. The particle flux has to be determined by 

FLUKA simulations, and the application defines the amount of resources (= number of configuration 

bits). The upper limit of applicability is given by the radiation damage due to cumulative effects, but 

such fluences will not be accumulated due to the limiting flux given by the SEU rate. 

Since the dependence of the SEU cross section on the technology is a priori unknown and cannot be 

sufficiently simulated (at least not today), it has to be measured by an accelerated method. FPGAs are 

usually irradiated in neutron and proton beams at different energies with fluxes much larger than the expected 

flux in the experiment. Such data for the ALTERA APEX20K400 and the XILINX XC2VP7 have 

been collected at the Oslo cyclotrone laboratory and the cyclotrone at TSL in Uppsala [198]. First results 

on the active partial reconfiguration of the XILINX FPGAs under irradiation are promising. However, 

although the use of SRAM FPGAs in radiation fields seems manageable, cross section measurements of 

the selected technology and the verification of the reconfiguration scheme under irradiation are mandatory.

236 Common Front-End Electronics Aspects

10 Data Acquisition, Event Selection, Controls, 

On-line/Off-line Computing 


Many of the signatures pursued with the CBM experiment are based on rare processes. To achieve an 

adequate sensitivity, the detector systems are designed to operate at interaction rates of up to 10 MHz 

for A-A collisions and up to several 100 MHz for p-p and p-A collisions. It is the task of the data 

acquisition and event selection system to identify the candidate events for the physics signals under 

study and send them to archival storage. The most challenging aspect is here the measurement of open 

and hidden charm production in heavy ion collisions down to very low cross sections. The D mesons 

will be identified via the displaced vertices of their hadronic decays, the decision for selecting candidate 

events thus requires tracking, primary vertex reconstruction, and secondary vertex finding in the STS. In 

addition, the system has to be configurable to handle a wide range of physics signals, ranging from D 

and J/ψ in A-A collisions over low-mass dileptons in p-A collisions to ϒ in p-p collisions. 

The conventional system design with triggered front-end electronics allows to keep the event information 

for a limited time, usually a few µs, in the front-end electronics while a fast first level trigger decision 

is determined from a subset of the data. Upon a positive trigger decision, the data acquisition system 

transports the selected event to higher level trigger processing or archival storage. A system with such 

a fixed trigger latency constraint is not well matched to the complex algorithms needed for a D trigger, 

especially in the case of heavy ion interactions, where the multiplicities and thus the numerical effort 

needed for a decision varies strongly from event to event. 

The concept adopted for CBM will use self-triggered front-end electronics, where each particle hit is 

autonomously detected and the measured hit parameters are stored with precise timestamps in large 

buffer pools. The event building, done by evaluating the time correlation of hits, and the selection of 

interesting events is then performed by processing resources accessing these buffers via a high speed 

network fabric. The large size of the buffer pool ensures that the essential performance factor is the total 

computational throughput rather than decision latency. Since we avoid dedicated trigger data-paths, all 

detectors can contribute to event selection decisions at all levels, yielding the required flexibility to cope 

with different operation modes. 

In this approach we have no physical trigger signal, which prompts a data acquisition system to read a 

selected event and transport it to further processing or storage. We thus avoid the term ’trigger’ in this 

chapter. The role of the data acquisition system is to transport data from the front-end to processing 

resources and finally to archival storage. The event selection is done in several layers of processing 

resources, reminiscent of the trigger level hierarchy in conventional systems. 

One consequence of using self-triggered front-end electronics is a much higher data flow coming from 

the front-ends on the detector. For CBM a data rate of about 1 TByte/sec is expected. However, communication 

cost is currently improving faster over time than processing cost, an observation sometimes 

termed Gilder’s law, making such a concept not only feasible but also cost effective. 

237

238 Data Acquisition, Event Selection, Controls, On-line/Off-line Computing 

10.2 Overall Architecture 

The communication and processing needed between the front-end electronics, generating digitized detector 

information, and the archival storage, where the complete context of selected candidate events 

is recorded, can be structured and organized in several ways. The solution described in this chapter is 

guided by two principles: processing is done after event building and it is done in a structured processor 

farm. It is well adapted to the type of processing needed in the CBM experiment and leads to a 

straightforward and modular architecture. 

FEE 

CNet 

BNet 

PNet 

HNet 

Detector 1 Detector 2 Detector N 

to high level computing 

September 9, 2004 CBM FEE/DAQ/Trigger - System and Network Architecture, Walter F.J. Müller, GSI 1 

Figure 10.1: CBM overall data processing architecture 

A logical data flow diagram is shown in Fig. 10.1, indicating the data sources and processing elements 

as boxes and every form of interconnection networks as ovals. The main components are: 

• Front-end electronics (FEE): The front-end detects autonomously every particle hit and sends 

the hit parameters together with a precise timestamp and channel address information over the 

concentrator network (CNet) to a buffer pool, a more detailed description was already given in 

sections 9.1 and 9.3. 

A rough estimate for the data volume generated by a detector channel can be deduced from typical 

CBM operation and detector parameters: 10 MHz interaction rate, 10 % occupancy for central 

collisions, a ratio of 1/4 for minimum bias to central multiplicity, and a typical cluster size of 

3 fired electronics channels per physical particle hit gives a channel count rate of about 750 kHz. 

Assuming 8 byte per hit yields a data flow of about 6 MB/sec and channel. For a typical FEE unit 

with 16 channels this results in a data rate of 100 MB/sec which can be transported over a single 

GBit serial link. 

• Clock and time distribution (TNet): The timestamps of each hit are used to associate hits with 

events but also in drift and flight time measurements. Thus not only a time scale, in practice a 

frequency, but also information about the absolute time has to be communicated to all front-end 

TNet


units. The most stringent requirements come from the START and RPC detectors, where the 

contribution from the clock jitter should be below 25 ps sigma. 

The most straightforward approach is to distribute a common clock frequency and to provide a 

mechanism for broadcasting information with clock cycle precise latency to all units. The minimal 

required functionality is a global clock reset at the begin of the measurement, or alternatively, 

distribution of tick marks every second as provided by the planned campus-wide frequency and 

time normal. 

The TNet is thus a dedicated broadcast network, connecting a central controller logically with 

all front-end units. The last hop to the front-end units may be implemented with the part of the 

CNet infrastructure, as indicated by the connection of TNet to CNet in Fig. 10.1 and discussed in 

sections 9.8.1 and 9.9.3.4. 

• Concentrator Network (CNet): The role of the concentrator network is to collect the data from 

the individual front-end units and aggregate the traffic on a set of high speed links which connect 

the detector with the area where the data buffers and the data processing is located. A rough 

estimate for the total data rate is 1 TB/sec which could be finally transported off the detector with 

about 1000 links with 10 Gbps each. 

The simplest implementation of the CNet is a collection of independent concentrator trees, one 

for each high speed link. However, a better load balancing or an appropriate degree of failure 

tolerance is likely to call for a more connected topology. 

In addition to the hit data transport from front-end to buffering, other communication tasks like 

control traffic or time distribution can be handled by the CNet infrastructure. Such an integrated 

approach is especially useful in conjunction with low-cost optical links (see section 9.7.3 and 9.8) 

and discussed in detail in section 9.9. 

• Active Buffers: The next stage in the data flow is a large buffer pool. The units are indicated as 

magenta boxes in Fig. 10.1. They are dubbed ’active buffers’ because the data is not only stored 

but potentially also reformatted and reorganized. They are also hand-over points between different 

types of networks, thus logically separating them and allowing to use different technologies in 

CNet, BNet, and PNet. 

• Build Network (BNet): The data arrives from the detector in about 10 3 parallel streams, each 

reflecting a small section of a detector sub-system. For the event selection processing, the information 

of an event has to be assembled in a farm node. This data reorganization is performed by 

the build network and the active buffers. 

In a conventional system, a trigger already defines the context of an event, so all further data 

processing and transport can be organized in terms of events starting at the FEE. In our case, the 

FEE sends a stream of time-stamped hits, and it is one of the tasks of the data processing, to first 

identify at what times interactions occur, and in a second step, to associate the hits with those 

events. 

This event tagging processing can be done before or after data traverses the BNet. In the first 

case, event tagging is handled in the active buffers, and the entities being assembled in the BNet 

transfers are indeed events. In the second case, only the timestamp information is available, and it 

is thus natural to assemble all the data of a time interval. 

A strict event-by-event approach would lead at the nominal Au+Au interaction rate of 10 MHz to 

a message rate of 10 10 messages per second with an average message size of 100 Bytes. However, 

because the transfer latency is uncritical, it is possible to choose a bigger dispatching unit, either 

an interval of events, or in the simplest case, a time interval containing a significant number of 

events. This aggregation reduces the message rate, increases message size, and because event


size fluctuations average out, also yields a more uniform message size distribution. A reasonable 

choice is an event interval of about 100 events or equivalently a time interval in the order of 10 µs, 

which also matches well with the anticipated frame times of a MAPS detector system. 

The simplest solution is a time interval based build logic with a shaping and traffic scheduling 

setup similar to the one developed for the LHCb first level trigger [195]. A more involved solution, 

which supports building event intervals and the suppression of event incoherent backgrounds, is 

described in section 10.3.1. 

Fig. 10.1 indicates that source as well as destination of a BNet transfer is an active buffer. They 

implement the protocol used on the BNet and are responsible for organization of the data flow, in 

particular for traffic shaping and appropriate scheduling of transfers. Because the actual traffic seen 

by the BNet can be controlled to a large degree and adapted to a given networking technology, it is 

assumed that the BNet can be based on a commercial off-the-shelf (COTS) technology. Plausible 

candidates are Ethernet, Infiniband, or ASI. 

Fig. 10.1 only indicates the logical data flow, not a concrete network topology. For one, it is 

possible to factorize the network in several ways, which allows to build the BNet with a set 

of medium-sized switches and thus to exploit the usually significantly better price/port ratio of 

smaller switches. Also, it is possible to merge the functionality of the two active buffer layers, one 

interfacing CNet to Bnet and one interfacing BNet to PNet, into a single entity, leading to a system 

with half as many BNet ports and bidirectional traffic on all BNet links. Last but not least, the star 

topology with centralized switches can be replaced with other structures, like a 2D- or 3D-torus 

topology with distributed switches. 

• Processing Resources: The first level of event selection processing has to handle the full event 

rate, and depending how many detector sub-systems are involved in the decision, a substantial fraction 

of the total data volume. A very rough estimate shows, that processing a data flow on the scale 

of a TByte/sec is likely to require a computational bandwidth on the scale of 10 15 operations/sec. 

With todays technology, the most promising approach is a hybrid system using a combination of 

hardware processors, implemented with programmable logic components like FPGA’s, and software 

processors, implemented with commodity PC’s. The kernels of algorithms which allow 

highly parallel execution are done in hardware processors, the rest in software processors. The aim 

is to execute most of the operations on hardware processors, which offer the best price/performance 

ratio for computational bandwidth, but to keep most of the code volume on software processors, 

which offer much easier program development and maintenance. 

Since programmable logic devices are essentially arrays of simple structures, it is expected that 

they scale well and thus density and speed will improve as the underlying silicon technology 

evolves (see also section 9.10.2). The development for software processors over the relevant time 

scale till the design freeze of the CBM data processing is likely to be more complex. The evolution 

of single processor speed has reached apparent limits of complexity and power dissipation, calling 

for changes in concepts and architectures. This trend is already apparent in recent developments 

like the compute ASIC for IBM’s Blue Gene system or the STI cell processor. 

It is also expected, that conventional fixed instruction set processor and programmable logic concepts 

will be coupled, resulting new forms of configurable computing, like processors with dynamically 

adaptable instruction sets. A first product in this emerging segment are for example the 

Stretch S5000 series CPUs [200]. 

Nevertheless, the rest of this chapter is formulated in terms of physically separated hardware and 

software processors because these are the mainstream products available today and the near future. 

The overall architecture is, however, easily adaptable to more integrated forms of configurable 

computing.

10.3. Communication and Processing Architecture 241 

• Processing Network (PNet): The processing resources are grouped in farm nodes. Each farm 

node is organized around a local processing network which provides the communication between 

the associated processing resources and active buffers, which act as central data repository and as 

gateway to the BNet. The PNet is thus structured into many local networks. 

The number of hardware and software processors aggregated to one farm node is determined by the 

amount of resources needed to efficiently handle all the algorithms needed for an event selection 

decision. Each hardware processor will have a dedicated configuration to execute a particular 

algorithm, a total about a dozen different configurations may be needed. A rough estimate gives, 

that an about equal number of software processors is needed for a balanced load of the whole 

system. 

It is expected that the resources of a farm are concentrated in a crate or are at least in close proximity. 

The PNet can therefore use technologies designed for short distance interconnects, plausible 

candidates are from todays perspective PCIexpress or ASI. As stated already for the BNet, 

Fig. 10.1 only indicates the logical data flow, not a concrete network topology. The PNet can be a 

homogeneous, single technology, switch based star network as suggested by the figure, but many 

other topologies are possible, some are discussed in section 10.3.2. 

• High-level Network (HNet): The task of the event selection processing described up to now is 

to perform a first reduction, similar to the ’level 1 trigger’ in conventional systems. A further 

reduction will be needed to reduce the data volume to a level suitable for archival storage. This 

will be handled by a more conventional processing farm (see section 10.6). The HNet provides the 

connection to this high-level computing. 

10.3 Communication and Processing Architecture 

10.3.1 BNet - Build Strategies 

The concentrator modules store the hit data in active buffer modules with functionality data dispatcher. 

These modules are then the senders into the BNet. At the other side of the BNet are active buffer modules 

with functionality event dispatcher. The task of the BNet is logically simple: the data of one event 

distributed over all data dispatchers, must be assembled in one event dispatcher for further processing 

(see Fig. 10.2). Nearly all experiments of the CBM class have solved this task. The special situation 

at CBM is that in the data dispatchers there is no event information yet. One possible method to get 

this information is to analyze the multiplicity over time of the silicon trackers. This means that the 

multiplicities of the ca. 50 STS concentrator modules (one or two layers of the STS system) must be 

summed up per time interval (about 2 ns) in subsequent bins of a multiplicity histogram. The time 

interval of an event can then be recognized by multiplicity signatures in this histogram. Each event gets a 

unique tag number. The association between event time intervals and tag numbers is called event tagging. 

Event tagging is used in the event building mechanism. 

It is assumed that a peer to peer connection in a conventional hierarchically switched network can transport 

1 Gbyte/s. It has been proven that currently available 10 GE switches are capable to switch full data 

rates even with 64 byte packets without loss. 

To avoid confusion in the following we mention here that there are two time systems: the intrinsic time 

is defined by the time stamps inside the data streams as delivered by the TNet. The scheduler time (local 

time of the active buffers) specifies when the data dispatchers have to send data to an event dispatcher. 

Time intervals in the data stream like epochs refer to the intrinsic time. These intervals can be recognized 

in each data channel independently by the time stamps, i.e. epoch markers. Each data dispatcher can 

determine the amount of data belonging to an intrinsic time interval. Intrinsic time intervals are time


FEE: front end electronics 

DD: data dispatcher 

HC: histogram collector 

BC: BNet controller 

ED: event dispatcher 

FEE CNet 

50000 

10 

HC BC 

1 

logical 

physical 

active buffer active buffer 

DD ED 

BNet 

1000 

Figure 10.2: BNet: logical overview. 

frames and time slices (sequential set of time frames). The schedule time determines when the data of a 

time slice has to be sent (data transfer buffer). The time interval to send this data is called time slot. 

10.3.1.1 Two approaches: time frame or event dispatching 

The event tagging can be done at two places: behind the BNet (frame switching) or before the BNet 

(event switching). In the first case the determination of the data transfer buffers to be distributed through 

the network is based on fixed time frames (intrinsic time). A time frame can be recognized by epoch 

time stamps in each data stream (variable data size!). Several time frames may be bundled into a time 

slice (data transfer buffer) by the BNet-controller (see Fig. 10.3). Because of data duplication at the time 

frame boundaries the time slices should be at least some 10 µs (hundreds of events). The full data rate has 

to be sent through BNet. However, no communication is needed for histogramming. The event tagging 

and hits association is then done locally in the event dispatchers. The second case - tagging the event 

Detector data buffer memory 

align 

time frame 

epoch or tagged events 

~ 10 µs 

1000 

5 frames = 1 time slice 

i [Kbytes] sent to one ED 

~ 50 µs : 50 Kbyte 

Figure 10.3: BNet: data dispatcher: detector data buffer structure. 

before switching - opens the chance to reduce the BNet throughput by suppressing background and by 

selecting events of a special multiplicity signature. Some of the data dispatchers get the additional task 

to generate and summarise the multiplicity histograms. The BNet-controller must determine the event 

tags before calculating the schedule. 

However, the traffic scheduling is similar in both cases. A decision between frame switching and event 

switching cannot be made in the current phase. Instead, both are investigated in the simulation setups. 

From experiment requirements it might turn out that both approaches are needed for different event 

signatures. 

PNet


10.3.1.2 Data flow due to event tagging 

Let us consider all kinds of data traffic, which is required to perform event tagging and event building. 

Our main approach is to keep all kind of traffic inside BNet. Therefore in the following we give some 

calculations about the net load produced by different traffic sources. 

The entry point to BNet are data dispatchers, which keep the input stream of (intrinsic) time stamped 

hit data coming from the associated concentrator module in detector data buffers. The data dispatchers 

corresponding to STS must generate local multiplicity histograms, which should be then summed up to 

detect events. One data dispatcher producing histograms with a bin size of 2 ns and 1 byte/bin generates 

a data rate of 500 MBytes/s. One can not afford to transport such noncompressed data over the BNet. 

But such local multiplicity histogram will have a lot of empty bins. Therefore, such histogram can be 

efficiently compressed down to an outgoing data flow of 100 MBytes/s per data dispatcher. 

It is not plausible to send all compressed histograms from 50 data dispatchers to one single end point. 

Therefore additional histogram collectors should be used to combine intermediate sum histograms from 

5-8 data dispatchers. About 7-10 histogram collectors will be required. Histograms from each histogram 

collector should be delivered to one end point, called tag generator. These histograms may not be compressed 

so efficiently as individual ones. Therefore another compression approach can be considered. In 

such intermediate sum histograms one expects more clear peaks, corresponding to single events. Therefore 

a histogram collector can locate these peaks and transfer them to the BNet-controller. One expects 

not more than 1.5 peaks per event or about 60 Mbyte/s data rate (supposing 4 bytes/peak in average). 

The tag generator should get peak information from all 7-10 histogram collectors and produce event tags. 

Each event will be defined by short time interval of several ns, where the STS multiplicity is above some 

threshold. Time shifts between subsystems must be taken into account. Each event gets its individual 

tag, which will be used in event building scheme. 

The event tagging approach implies that not individual event fragments, but rather groups of subsequent 

event fragments (event groups) should be dispatched in the BNet during data transfer. Based on event 

multiplicities the BNet-controller can balance the size of defined event groups. Such grouping should 

reduce the size variation of the data transfer buffers dispatched in the BNet. 

Once events are tagged and event groups are defined, the corresponding information should be distributed 

to all data dispatchers. Each event requires about 2 bytes for coding which results in about 20-30 

MBytes/s from the BNet-controller broadcast into the BNet. That results in about 30 GByte/s traffic at 

the end nodes (data dispatchers). 

When the data dispatcher has received the event tagging information it selects by the time stamps appropriate 

data pieces from the input buffer. At that moment most of noise hits and non interesting events can 

be thrown away and only sorted data (which is smaller in size) is kept in the detector data buffers. 

10.3.1.3 Data flow due to scheduling 

The main task of the BNet-controller is to balance the data transfer of the network and utilize full bandwidth 

of peer to peer connections. The BNet-controller should know the exact sizes of data packets to be 

transferred from data dispatchers to event dispatchers. Therefore, after data dispatchers produced sorted 

data for event groups, data sizes for each group of events should be send to BNet-controller. One expects 

about 10 5 groups/s producing 200 KBytes/s of information traffic sent by each data dispatcher and about 

200 MBytes/s received by the BNet-controller. 

Based on the received information about the event group sizes, the BNet-controller produces a schedule, 

which prevents conjunction, blocking, or data loss in the BNet. The schedule includes the exact sequence 

of transactions for each data dispatcher, synchronized by time slots (scheduler time). The schedule will


be described in the following in more detail. Once the schedule is produced, it must be distributed to all 

data dispatchers. This is done in two phases. In the first phase the individual schedule is transferred to 

each data dispatcher producing about 200 KBytes/s resulting in a total of 200 MBytes/s outgoing from 

the BNet-controller. 

In the second phase the schedule timing information (scheduler time, about 200 KByte/s) is broadcast to 

all data dispatchers. This portion includes timing, when each transaction in schedule should be started. 

This schedule timing information is distributed right before the schedule can be executed. 

10.3.1.4 Data flow summary 

During schedule execution each data dispatcher according to its individual schedule sequence sends 

data packets to an event dispatcher, compressed histograms (if required) to a histogram collector, or data 

packets information to the BNet-controller. At the same time a data dispatcher may receive event tagging 

data and schedule for the next transactions. Table 10.1 shows a short summary of all traffic in the system. 

Traffic type Sender Receiver Sender Receiver Total 

Single histograms data dispatcher histogram collector 0.1 0.7 5.0 

Peak data histogram collector tag generator 0.1 0.8 0.8 

Tagging brdcst BNet-controller data dispatcher 0.03 0.03 30.0 

Size information data dispatcher BNet-controller 0.0002 0.2 0.2 

Sched. destinations BNet-controller data dispatcher 0.2 0.0002 0.2 

Sched.timing brdcst BNet-controller data dispatcher 0.0002 0.0002 0.2 

Data transfer data dispatcher event dispatcher 0.9 0.9 900 

Table 10.1: List of traffic types in the system and data rates (in GBytes/s). 

It can be seen, that the sum of all meta data traffic is only a few percent of the main data traffic. Therefore 

it seems to be promising to develop a mechanism to mix all kind of traffic in one common network. The 

intention is to keep all traffic types inside BNet to reduce construction cost. 

10.3.1.5 Time frame switching 

This mode will be required, when event tagging via multiplicity histograms is not possible, i.e. when 

events have a very low multiplicity, or when the event rate is too high. In that case not a group of events, 

but intrinsic time frames will compose a time slice to be dispatched into the BNet. This mode can be 

supported with very slight modifications of the scheme proposed so far. 

First of all, histogramming can be kept as it is, but the histogram bins can be increased from 2 ns to 100- 

200 ns scaling down the size. The histogram is not used for peak detection but rather to get a guess about 

the data size of the time slice. The tag generator obtains a histogram over 50 STS channels which shows 

the average hit density over a certain time interval. From that histogram the tag generator can produce 

variable time slices to balance the average data size of the slices. By the same method, as event tags, 

time slice definitions can be distributed to the data dispatchers. There the correspondent data packets can 

be produced. From that point on the remaining schedule sequence, which includes info traffic, schedule 

distribution and schedule execution algorithms is the same. Instead of an event group identifier a time 

slice identifier is in the schedule. Finally, the event dispatcher modules should execute in this mode an 

additional algorithm to tag events (if it will be possible). Most probably, such modified scheme can run 

on the same hardware. Only another version of software, running on data dispatcher and event dispatcher,


should be used. 

10.3.1.6 Network topology 

The BNet should provide full connectivity between 1000 data dispatchers and 1000 event dispatchers. 

For the implementation of a logical 1000x1000 switch several approaches are possible. We intend to 

use the network bidirectional to reduce the number of components and final cost. For that one has to 

combine the data dispatchers (mainly source of data traffic) and the event dispatchers (consumer of data 

traffic) on the same device (active buffer) marked as DD/ED in Fig. 10.4. Then they can share one I/O 

port, connected to BNet. 

The logical 1000x1000 switch should be factorized. As one possibility the principle of a star topology 

is shown in Fig. 10.4. The n ’outer’ ports of the switches are connected to active buffer modules 

(DD/ED). The ’inner’ n-1 ports connect the switch with the other n-1 switches. As a result n*(n-1)/2 

interconnections between switches are required. 

For example: n=8 means that 64 data channels can be implemented and 28 interconnection are required. 

For n=32, 1024 channels can be implemented and 496 interconnection are required. One 32x32 switch 

could again be split into 12 6x6 switches for cost reasons, resulting in 384 switches. This case, however, 

introduces more layers in the hierarchy and needs further investigation. 

CNet 

n -1 ports 

HC/DD 

n * (n -1) / 2 bidirectional connections 

TG/BC 

BNet controller 

n 

switch n × n •• • • switch n × n 

n -1 ports 

•• • •• • • n 

n -1 H: histogrammer 

TG: event tagger 

DD/ED 

HC: histogram collector 

CNet 

H 

PNet 

BC: scheduler 

DD: data dispatcher 

ED: event dispatcher 

Figure 10.4: BNet: logical star topology. 

10.3.1.7 The data dispatcher / event dispatcher module 

DD/ED 

active buffer 

CNet PNet 

The combination of data dispatcher and event dispatcher in one active buffer module allows to fully utilize 

the I/O channel connected to the BNet. The active buffer has one input channel from the concentrator 

module and one output channel to the event processing net (PNet). The main functions of such active 

buffers are: buffering of input data, resorting data to event groups (data transfer buffers), sending data 

and info packets according provided schedule, and receiving and combining data for event groups. 

The active buffer also should include a functional block to accumulate the local histogram (letter H in 

Fig. 10.4) when the data dispatcher is assigned to an STS data channel. For other modules this part can 

be deactivated.


The proposed scheme of event tagging requires another function block, the histogram collector. One 

possible approach is to build it as independent device. Another approach is to implement the functionality 

of a histogram collector on the event dispatcher part of our combined module. It means, that several (up 

to 8) active buffers in the system will not perform event building, but will combine histograms over 

several STS data channels. Therefore such DD/HC modules will not be event dispatchers. 

10.3.1.8 BNet-controller module 

As it is shown on the network scheme in Fig. 10.4, this module will have two connections to BNet. One 

of these connections will be used to receive data from the histogram collectors, the other will be used to 

receive information data from DD/ED modules and distribute scheduling packets back. 

The BNet-controller contains two functional parts, which are mostly independent: event tagging and 

schedule production. The first task is relatively simple and only requires, that peak data is delivered 

continuously from the histogram collectors. The major and most demanding task is to produce the 

schedule, which guarantees a smooth transfer not only of event data, but also of all other meta data 

distributed over the system. 

The scheduler knows a priori how many data has to be transferred from every source in the system. 

Therefore it can assign the appropriate time for the transfer to avoid collisions in the network switches 

and overruns of pipelines. The scheduler also assigns some time slots (holes) in the main data traffic, 

which are used for different kinds of metadata transfer. 

10.3.1.9 Traffic shaping and schedule 

Let us consider one particular setup and discuss different aspects of the schedule. When events are 

tagged, they are combined into groups. Most probably a single group of events will include about 500 

events, covering about 50 µs of intrinsic time, which we call time slice as shown in Fig. 10.3. One 

expects about 50 KBytes of data, which can be assigned in each data dispatcher to that group. Data at 

the group boundaries may be assigned to the two subsequent groups. All this data (50 KByte over 1000 

data dispatchers results in 50 MBytes) should be finally collected in one single event dispatcher, where 

full events can be build. 

At any (scheduler) time all data dispatchers should send data but only one data dispatcher should deliver 

data to one event dispatcher, where the events of the specific group are built. Therefore one needs at 

least the data of 1000 (intrinsic) time slices to start the delivery of data from all 1000 data dispatchers 

simultaneously. 

The time to deliver all data of a single schedule is called super cycle. One super cycle which in our 

case is about 50 ms intrinsic time (from 1000 time slices or event groups of about 50 µs) covers the full 

schedule for all data dispatchers to deliver data to all event dispatchers. The scheduler time interval to 

send the data of one intrinsic time slice is called time slot. 

At each time slot the scheduler assigns a reciever for each data dispatcher in such way, that the produced 

traffic will not overlap in the network. For example, if a data dispatcher connected to switch number 1 is 

sending data to an event dispatcher connected to switch number 2 no other data dispatcher connected to 

switch 1 will be scheduled to send data to any event dispatcher connected to switch 2. This condition is 

a result of the single connection line between switch number 1 and switch number 2. The schedule must 

always fulfill that condition. 

In such approach all data dispatchers should finish their data transfer during one time slot before the next 

time slot can be started. Therefore, if packets sizes vary, most of data dispatchers sending smaller data


packets will be silent while the biggest data packet is injected in the net. In the extreme situation, when 

the packet sizes vary by about 50 %, the data throughput will slow down by factor of 2 or even more as 

can be seen in Fig. 10.5. For that case an optimized sorted schedule can be generated. It tries to transfer 

data of approximately the same size at the same time slot. Intensive calculations are required to produce 

such a schedule, but it allows to keep the utilization of the network above 90 % even if the variation of 

packet sizes exceeds 50 %. 

Network utilization [%] 

100 

90 

80 

70 

60 

50 

40 

Network utilization by different schedule methods 

sorted 

not sorted 

0 10 20 30 40 50 

Buffer size variation [% of data size] 

Figure 10.5: BNet: Optimization of data dispatching. 

The data transfer in each time slot is divided into small transfer units of fixed size to produce a payload 

equilibrium in the network. The transfer unit size could be about 1.5K, which is an upper limit for current 

standard for Ethernet. In our case one time slot is divided into about 30 transfer units, which should be 

delivered to one destination. 

Of course, such schedule requires, that the data transfers should be very well synchronized across the 

BNet. All data dispatchers must have a good synchronisation of a system clock. At least a precision 

of µs should be achieved. Such a system clock could be derived from the concentrator’s data channel, 

because the concentrators will be connected to the time distribution system with a time presison of some 

ns. Another possibility is to provide a dedicated time distribution system connecting all data dispatchers. 

A precision of one or even several µs should be good enough. For this overlapping time the data must 

be stored inside the switch. Current 10 GE switches with a latency of about 20 µs at 1KByte packet size 

can deliver the full data rate. This means that they can buffer a significant number of packets in internal 

memory before the packets are delivered to the output. Therefore an uncertainty in sending time between 

data dispatchers can be smoothed by the internal switch buffers. An overlap of 1 µs results in one more 

packet of 1 KByte size to be additionally buffered in the switch, while the switch already keeps at least 

20 packets of the same size because of latency constraints. 

During a time slot of 50 µs a maximum of 33 packets of 1.5 KByte can be sent. Normally all these 

packets should be used to send data from data dispatcher to event dispatcher. But in certain situations the 

scheduler marks some of these packets not to be used (assigned as holes) allowing to transfer meta data in 

different directions synchronized with the main traffic. Depending on the kind of traffic (histogramming, 

information, scheduling) different algorithms how such holes should be distributed are used. For a single 

data dispatcher in every time slot not more than 15 % of holes are needed. In the overall system only


about 1-2 % of holes are required to distribute the meta data. 

Let us consider the situation of histogramming traffic. One needs to deliver about 100 MBytes/s from 

a data dispatcher building a local histogram to a certain histogram collector. While these devices are 

connected to the same switch, the data dispatcher can send the compressed histogram to the histogram 

collector at the time, when a hole in the main data traffic is scheduled. The only task of the scheduler is 

to distribute holes in such way, that the histogramming traffic is not crossing at the receiver (histogram 

collector) side. 

A different approach is used, when information data should be delivered from a data dispatcher over the 

whole BNet to the BNet-controller. In that case the scheduler assigns a hole in the data traffic of those 

data dispatchers which are sending data into an event dispatcher connected to the same switch, where 

the BNet-controller port is connected. Then this hole will be used for information data. All packets (data 

and meta data) from that data dispatcher will travel over the BNet together up to the last switch, where 

they will be split by address selection (data to event dispatcher, meta data to BNet-controller). 

10.3.1.10 Simulation of event buidling 

Using the SystemC simulation package (http://www.systemc.org) several models have been set up to simulate 

and investigate the performance of the described setup. The simulation includes function models 

for components like data dispatcher, histogram collector, tag generator, BNet-controller, and event dispatcher. 

Connectivity between the modules is modeled with a special interface module (simulates piece 

of wire), which includes propagation speed and data transfer rate. Models for network switches with 

limited buffer capacity were also introduced. A full algorithm for schedule building was implemented. 

A setup with 100 end nodes (DD/ED modules) was tested. Histogramming for event tagging was done 

over 16 channels. A time slot of 3 µs was used. The super cycle size was about 300 µs. A simulation for 

10 ms, which includes about 30 super cycles, takes about 25 minutes on an Athlon 1800+ XP computer. 

Each time slot was divided on 7 packets of about 450 bytes. The event generator provides a mixture 

of events and uncorrelated noise. A sustained bandwidth utilization of 79% (netto) for data traffic was 

achieved. At the same time the overall occupancy of the network connections between the switches was 

greater than 90%. 

Our simulation shows that the latency of event tagging (the time between event hit detection and event 

tag production in the tag generator) is negligibly small. In the tested setup it was only about 15 µs. The 

latency of event building (the time between event hit detection and event combination in event dispatcher) 

was about 1 ms, which is roughly the duration of 3 supercycles. For that time period event data must be 

kept in the detector data buffer of data dispatchers. In a realistic setup with a supercycle size of 50 ms a 

buffer depth of 150 MBytes per data dispatcher will be required. 

10.3.1.11 MAPS problematic for event buidling 

The MAPS is read out continuously with a cycle of about 10 µs (see 2.2.3). That means that up to 200 

events are distributed in two subsequent MAPS frames, i.e. the event data is not in time order. Therefore 

tagged event fragments of the other channels have to be combined for event building with event fragments 

of at least two MAPS frames. When event fragments are dispatched to an event dispatcher the full MAPS 

frames must de sent to this event dispatcher too, i.e. the MAPS frames must be copied and sent to several 

event dispatchers. For small event groups, which cover about 10 µs, one has to duplicate at least 3 MAPS 

frames. This will populate MAPS traffic by factor of 3 and overall data traffic by 20%. But for realistic 

setup, when a group covers 50 µs, only two boundary MAPS frames will be duplicated and overall traffic 

increase will be only about 5%.


10.3.1.12 Broadcast problematic in BNet 

The main problem of broadcast is the generation of traffic simultaneously on a big number of receiving 

nodes, exploding traffic over the net. If there is any other traffic distributed at the same time the broadcast 

immediately causes collisions and loss of packets. However, in BNet all traffic is controlled by the BNetcontroller. 

By proper schedule timing the BNet-controller can provide delays between transactions, in 

which none of the nodes in the system will send any data. During such delays broadcast packets can 

be injected in the BNet. As long as there are no other transactions in the net, broadcast packets can 

be smoothly delivered to all end nodes. The total sum of broadcast traffic, going out of the BNetcontroller 

should not exceed 30 MBytes/s or 3% of the avaliable bandwidth. Therefore one needs only a 

few delays in every schedule, which sum up to about 3% of a supercycle. 

It has to be proven that the network technology used to construct the BNet will provide a reliable multicast 

(broadcast) mechanism at least when no other kind of traffic is going on. If this cannot be achieved, 

a two stage peer to peer connection should be used to implement broadcast. A single source can populate 

traffic only between about 30 endnodes. One would need one additional module per 32 data dispatchers 

to distribute such "broadcast" data. This module can be connected to one spare port, left in each switch. 

All these 32 distribution modules should have a separate connection to the BNet-controller to get data, 

which should be broadcast. Such approach requires additional hardware, but excludes any broadcast in 

the BNet. 

10.3.1.13 Flow control and error detection 

All previous estimations were done with the assumption that the underlaying network protocols guarantee 

delivery of packets sent over the BNet. But one can also impose error detection for several kinds of traffic. 

Most of the network bandwidth will be used for data transfer. Therefore most probably data packets can 

be lost. For that case an easy, but efficient error detection can be proposed. In the same manner, as each 

data dispatcher gets its schedule about the data to be sent, each event dispatcher can get a schedule about 

data to be delivered to it. The event dispatcher can time out after several µs when the announced packets 

did not arrive. Error detection information can be delivered with other info traffic to the BNet-controller. 

Later lost packets can be rescheduled again. 

A similar approach can be applied for the traffic going to/from the BNet-controller. It knows exactly 

which module should send data to it and when. Thereforfe it can detect lost packets. 

Generally, error detection can be implemented for all kind of traffic, but this may increase complexity of 

the system. Therefore, further investigations of transaction error probability and required error correction 

mechanisms should be done. 

10.3.2 First Level Farm Strategies 

In this section we give a motivation for the size of the first level farm system together with a description 

of one processor sub-farm. We show the principle function and the implementation of the components 

of a sub-farm. 

The detector outputs about 1 Tbyte/s of data via about 1,000 10 Gbit/s links. These data is stored, sorted 

according to epoches during their transfer through the building network, and stored again. Finally it is 

processed event by event. The storing and the processing is the main part of the function of the first 

level farm. A reasonable time for one epoch is a multiple of the MAPS readout time, that is in the 

order of 10 µs. To get all the readout frames of the MAPS detector that belong to one certain time it is 

necessary to merge data from two epochs. That’s why each sub-farm should contain the storage capacity


and processing power for several epochs, to minimize the additional data traffic. A reasonable number 

of sub-farms is between 64 and 128. Each sub-farm has to store and process data from 8 to 16 input 

links, what corresponds to 8 to 16 epochs, respectively. So only every eighth to sixteenth epoch has to be 

copied in two sub-farms. Of course these numbers are only typical values to show the feasibility of a later 

realization. In particular it is a special feature of this architecture that it is very flexible and adaptable to 

many different requirements. 

The size of the sub-farms needs not to be the same, it does not require the same type and amount of 

processors nor the same composite of them in the different sub-farms. This has many advantages, e.g. 

during commissioning, maintenance, update and running of the experiment. During start-up and commissioning 

sub-farms with general purpose processors, later on called software processors (L1/CPU), 

and other COTS components are used. Of course the processing power is reduced, but it offers a maximum 

of flexibility by testing and debugging all detector and front end problems by software. In a second 

step these sub-farms are expanded by special purpose hardware processor units (L1/FPGA) to speedup 

the processing. For maintenance during the running experiment or for an upgrade of parts of the system, 

individual sub-farms or parts of them can be switched off and replaced by loosing only one to two percent 

of processing power. Of course the L1/CPUs and networks are most probable COTS components, where 

the bulk is bought at the latest date. So the L1/CPUs of the sub-farms installed first may be different 

from the ones used finally. For the L1/FPGAs proprietary development is necessary. 

HLT physical analysis C++, Framework, GEANT 

L1/CPU physical analysis, C++, Framework, GEANT 

event selection 

L1/FPGA event selection C++, Verilog, VHDL 

Figure 10.6: Levels of software generations. 

Different levels of event selection will implement different algorithms according to required data reduction 

and hardware implementation (Fig. 10.6). Nevertheless all levels must be algorithmically compatible. 

The algorithms have to run within the framework being mutually replaceable 1 to make possible 

implementation of the same well tested and efficient algorithm in all levels, or to develop and monitor an 

algorithm which can be applicable only for a specific level of data selection. 

The time needed for processing algorithms for event selection using general purpose processors in GHz 

PCs, as usual today, is in the order of several hundreds milliseconds to several seconds, depending on 

the complexity of the algorithm and track multiplicity of an event. Due to increases in processing and 

integration technology a speedup of 50 to 100 is expected within the next 8 to 10 years. The processing 

time will shrink to a few milliseconds up to some tenth of milliseconds per event. But still an event rate 

of 10 MHz would require a total of 10,000 to 100,000 processors per algorithm. Even when divided into 

sub-farms, a realistic and reasonable size of a computer farm is about a factor 100 smaller. 

One solution to speedup algorithms is the coprocessing in FPGAs (field programmable gate arrays). 

According to Amdahls law about 99% of the algorithm has to be accelerated by hardware maximally to 

get a speedup factor of 100. This means that only one percent stays in software, that is most probable 

just the controlling of the algorithm. 

The task is to implement the required computing power and communication bandwidth at affordable cost. 

In order to meet this challenge one has to optimize the analysis algorithms and to execute them according 

to their computational and data access characteristics on the most effective compute architecture. Both 

the relevant computing and communication technology showed an extraordinary development during the 

last years which is expected to continue over the time-scale of the project. 

1 This must be valid at least for their C++ versions.


The most reasonable technology for the implementation of L1/FPGAs are reconfigurable logic devices 

like FPGAs. The FPGA technology gains the maximum benefit from the development in the chip industry. 

Because of their very regular internal structure FPGAs are, besides RAMs, the first chips that can be 

built in new technologies. General purpose processors are following later due to their higher complexity. 

It can be expected that FPGAs with a capacity well above 100k logic cells and clock rates in excess of 

500 MHz will become available at commodity prices towards the end of the time frame. This will allow 

to implement the complex algorithms needed for event selection in FPGAs. 

Additionally to the logical resources modern FPGAs also offer up to 20 10 Gbit/s serial I/O (SERDES) in 

silicon, together with embedded processors for monitoring, test and control. 10 Gbit/s links over optical 

or copper interconnects as well as the associated switching hardware are expected at affordable prices in 

the next few years. 

Scheduler 

Scheduler 

-Farm Farm 

L1/FPGA 

L1/CPU 

L1/FPGA 

L1/CPU 

L1/FPGA 

Detector 

L1/CPU 

AB 

PNet 

L1/CPU 

BNet 

Sub-Farm Sub Farm 

A sub-farm (Fig. 10.7) consists of different units. 

L1/FPGA 

L1/FPGA 

L1/CPU 

L1/FPGA 

L1/CPU 

L1/FPGA 

Detector 

L1/CPU 

AB 

L1/CPU 

BNet 


L1/FPGA 

L1/FPGA 

L1/CPU 

L1/FPGA 

L1/CPU 

L1/FPGA 

L1/CPU 

AB 

PNet PNet 

Figure 10.7: General structure of a sub-farm 

Detector 

L1/CPU 

BNet 


L1/FPGA 

L1/FPGA 

L1/CPU 

Farm 

Control System 

1. Active Buffer (AB): An AB is the interface between the detector link, the Built network (BNet) 

and the processor sub-farm network (PNet). Additional to the input/output of data from different 

networks, it has to store the data, and to process the event building. An AB can be split into three 

functions. First, it has to store the data coming from the 50 m to 100 m detector link and to output 

to the BNet. Second, it has to receive the sorted data according to epoches from the BNet and to 

store and output them event by event to the processors of the sub-farm. Before or after this second 

step it calculates a histogram of the timestamps of the data for the event building. Peaks in this 

histogram give the time of an event. The detectors with the best time resolution determine the 

exact time. Finally, it sends the sub-detector data from one event to the processing units. In this 

sense the AB also controls the processing resources of a sub-farm. 

The ABs are implemented using FPGAs for the processing of the data transfer protocols, the 

histogramming, and the control of an external memory for the data storage. 

2. Hardware Processors (L1/FPGA): In these units the algorithms needed for the first level event 

filtering are processed. To keep pace with the high detector event rate of 10 MHz these algorithms 

are implemented directly in hardware. As described earlier FPGAs are the obvious choice for 

this task. FPGAs combine the advantage of the flexibility of programming with the processing 

of an algorithm directly in a parallel hardware processor. The complexity of the FPGAs and the 

L1/FPGA 

L1/CPU


resulting system will be chosen in a way, that it is possible to process an input data stream from 

the network (e.g. 10 Gbit/s) in real time and implement an enclosed part or the entire algorithm in 

one L1/FPGA. On the other side the system has to be modular to connect the different algorithms 

to one event filter. Of course, the system has to be flexible enough to implement different types of 

algorithms according to the physical program of the experiment. 

3. Software Processors (L1/CPU): These units consist of general purpose processors like PCs or 

system-on-a-chip processors (SoC), possibly using L1/FPGAs as coprocessors. Most probable 

these processors will be COTS components. 

The L1/CPUs are used to process data from sub-detectors for a more accurate analysis at the end 

of the first and in the second filtering level. Additionally it is used to process global algorithms for 

high level filtering. The L1/CPUs integrated in the first level farm are the interface to the higher 

level and off-line farm and to the data archive. 

4. Processor Farm Network (PNet). The PNet connects the ABs, the L1/FPGAs and the L1/CPUs of 

one sub-farm. It has to be freely programmable although for a fixed algorithm only fixed pointto-point 

connections are necessary. The connections are only inside of one sub-farm, e.g. chip-tochip 

and board-to-board links with short distances (< 1 m). Especially, there is no dynamically fast 

switching all-to-all network needed, like the BNet. The bandwidth of a link should be in the order 

of 10 Gbit/s, because this is the planned on-line processing rate for the L1/FPGAs. Best choice 

for PNet is to use the build-in serial links in FPGAs and processor systems and connect them with 

COTS switches. PCIe-AS is a plausible candidate for a commonly used serial interconnect and 

thus for PNet. 

5. Network to high level and off-line farm: Most probable this network will be what is called Ethernet 

in 10 years. An expected data output rate is about 1 GByte/s per subfarm. 

10.3.2.1 System architecture 

The 10 MHz event rate is needed for the desired physical performance of D and J/ψ measurements. For 

the research concerning low-mass di-leptons and multi-strange baryons a minimum bias readout of 20k 

events per second, that is equivalent with an 1 GByte/s output rate of the first level farm, gives sufficient 

sensitivity. So the processing of D and J/ψ determines the system architecture of the first level farm. 

M1 

M2 

S1 

S2 

S3 

S4 

S5 

RICH 

D 

J/ψ J/ 

TRD, ECAL 

Figure 10.8: Filtering 

strategy for two different 

event types


For the processing of decays of D-mesons tracks have to be reconstructed from the STS detector data 

in order to determine the primary vertex. Then all candidates for secondary tracks have to be combined 

to determine the invariant mass with an assumed particle identity. As the primary event rate is 10 MHz 

the necessary tracking, fitting and vertexing has to be processed in real time using dedicated hardware 

processors (L1/FPGAs). This first filter step is expected to reduce the event rate by a factor of 100. So 

the following second filter step can be calculated using general-purpose software processors (L1/CPUs). 

In a high level filter step this L1/CPUs can be used to perform a global tracking and filtering strategy. 

For the J/ψ processing the situation is a little different, e.g. the data from the TRD detector is used for 

tracking. Using PT cuts, the electron candidates are determined and the invariant mass is calculated. 

No special vertexing is necessary. Additionally or alternatively the ECAL data can be used for a proper 

particle identification and energy measurement. 

The first level system consists of hardware units (ABs and L1/FPGAs) and of general-purpose processors 

(L1/CPUs) connected by a network (PNet), as depicted in Fig. 10.9. 

from detector to/from Bnet from detector 

Tracking 

AB AB AB AB AB AB AB AB 

to 

L1/CPUs 

... 

... 

L1/FPGA 

Event selection 

L1/FPGA 

Secondary vertex 

L1/FPGA 

L1/FPGA 

Primary vertex 

Pnet 

L1/FPGA 

L1/FPGA 

MAPS (layer1) 

L1/FPGA 

MAPS (layer 2) 

L1/FPGA 

Fitting 

L1/FPGA 

L1/FPGA 

L1/FPGA 

L1/FPGA 

TRD processing 

L1/FPGA 

L1/FPGA 

L1/FPGA 

L1/FPGA 

ECAL processing 

Figure 10.9: Block diagram 

of L1/FPGAs and their 

connection in one half of a 

sub-farm 

The number of ABs needed can be derived from the detector output data rate. Each AB unit is capable 

to store and process a 10 Gbit/s data stream. Assuming about 1,000 detector data links, this results in 

about 1,000 ABs needed for storage and processing. For each of the approximate 64 sub-farms therefore 

16 ABs are needed. During one epoch ca. 1,000 detector links output data with 10 Gbit/s. This data is 

stored in the detector data buffer memory of the ABs and later output to the BNet in a way, that BNet is 

capable to sort data according to epochs. The BNet outputs the sorted data again to the ABs, where it is 

stored in the epoch data storage. This takes 1000 × epoch time with one 10 Gbit/s link. 

The STS detector generates about 1/5 of all data. That means it also takes about 1/5 of the input time 

of the data to the AB, to output the STS data. Therefore four ABs can share one STS tracking unit. 

The STS tracking unit outputs the track parameters of found tracks. The number of tracks is about 1/5 

of the number of hits plus additional information, because it takes more bits to code a track than one 

hit. But still two STS tracking units can share one track fitting and vertexing pipeline. The track fitting


and vertexing is divided into several steps, according to several L1/FPGAs. The processing time for the 

fitting and vertexing is proportional to the number of tracks. 

With a mean event rate of about 10 MHz every 100 ns a new event occurs. Using 256 STS tracking units, 

25 µs per event are available. Assuming real time processing with a processing time proportional to the 

number of hits, and a typical min bias event size of 1,500 hits, this results in a available time of 15 ns/hit. 

So a pipelined design with a frequency of several 100 MHz, that seems to be realistic and realizable, even 

several clock cycles are available for processing a hit. 

For a further processing of a track, found by the tracking, a unit for the fitting of the track is needed to 

determine a more accurate momentum. Two other units are needed to process the track following through 

two layers of MAPS detectors. Another two units are used to calculate the primary and the secondary 

vertex. Finally one unit calculates the invariant mass and stores the resulting data for a later usage of the 

L1/CPUs for the second filter level. Summarizing this, per eight ABs, two STS tracking units, one fitting 

unit, two MAPS track following units, two vertexing units and one event selection unit are needed, see 

figure 10.9. 

The amount of data coming from the TRD detector is about the same as from the STS, therefore about 

the same amount of L1/FPGAs are needed for the tracking. Additionally to each tracking unit one unit 

is needed to calculate the momentum vector of the found tracks. 

The processing of the ECAL data is comparable simple, because mainly cluster finding is needed. From 

the ECAL output data rate it can be estimated that one L1/FPGA is needed per four ABs. A second 

L1/FPGA might be needed to combine the ECAL data with the processed TRD data. 

In total per eight ABs, eight L1/FPGAs are needed for the processing of STS data, four L1/FPGAs for 

TRD and four L1/FPGAs for ECAL. 

Each of this L1/FPGA units need about the same hardware resources, that’s how the algorithms, described 

in the next section, are developed. This leads to a modular system, where each hardware unit 

can be used for every part of the algorithm. Even the hardware needed for the ABs is about the same, 

except larger memory and different network interfaces are needed. That’s why most of the development 

needed to built the hardware for the ABs and L1/FPGAs is about the same, and most of the R&D and 

pre-prototypes are the same for both units. Also identical is the part to initialize and control the FP- 

GAs and memories of the AB and L1/FPGA units. To provide an easy to use interface for the user an 

additional microcontroller with Ethernet running Linux is foreseen. 

After a significant event reduction (factor 100 expected), the data will continue to proceed by the second 

level algorithms running in L1/CPU (Fig. 10.10). Being compatible with the first level reconstruction 

algorithms there is no need to rerun them, but only to improve quality of tracking and vertexing with 

higher precision of L1/CPU and in addition using data from other sub-detectors. Thus the first level 

L1/FPGA processors will play the role of coprocessors of the second level L1/CPU processors solving 

all time consuming combinatorial problems, such as track and ring finding. 

After another event reduction the selected events will continue to process at the high level farm where 

the algorithms will use data from all sub-detectors to achieve maximum efficiency of event analysis. 

To end up with a manageable system and board size of the hardware units, the proposal is to combine 

four hardware units on one board. An advantage of a larger board is that the infrastructure for power 

supply and controlling is needed only once per board. So on one AB board there are four AB units, and 

on one L1/FPGA board there are four L1/FPGA units. Assuming 64 sub-farms, there are 4 AB boards 

and 8 L1/FPGA boards needed per sub-farm. 

The reduction of the first filtering level is expected to be about 100. The input event rate for L1/CPUs 

therefore is about 100 kHz, or 10 µs per event.


Figure 10.10: Algorithms for the level-1, level-2 and high level selections as presented from top to bottom. Every 

next level of selection will improve results of the previous one using more data and having more time available. 

From processing time today, e.g. track finding in STS takes 200 ms, one can extrapolate the processing 

time needed for the processing in 8 to 10 years. According to Moores law there will be a speedup of 50 to 

100 concerning general purpose processors in the next 8 to 10 years. So the time needed for processing 

will be in the order of 10 ms. The amount of data coming from the TRD detector is about the same 

as from the STS, therefore about the same amount of L1/CPUs are needed for the tracking. A total of 

2,000 PCs can approximately solve the problem, that means that in each of the 64 sub-farms there are 

32 PCs. 

System-on-a-chip CPUs like BlueGene etc. may have a better performance concerning GFlops/ or 

GFlops/Watt than PCs, that’s why a solution using one or two 128 SoC processor boards per sub-farm 

may also be worthwhile. 

10.3.3 Implementation Plan for First Level Farm 

The implementation plan is based on the assumption that most of the CBM installation is done by end 

2013 and that 2014 is used for test and commissioning. For the first level farm hardware this means that 

the readout should be available by end of 2013, but the processing capacity is not needed 100%. This 

has the advantage that the components can be bought at the latest date in order to save money. 

Proprietary developments clearly are more critical than COTS components. To build, test and install


Activity Time period 

Adapt algorithms to detector changes 

C/C++ simulations 2005 - 2008 

VHDL/Verilog coding 2005 - 2008 

Implement algorithm in pre-prototype 2008 - 2009 

Adapt algorithms to real detector and commissioning 2010 - 2014 

R&D prototypes 2005 - 2007 

Pre-prototypes 2008 - 2009 

Fix architecture (type of FPGA, RAM, network) 2010 

Decision of COTS components 2011 

AB prototypes (8) 2010 

AB small series (32) 2011 

AB production 1 (96) 2012 

AB production 2 (180) 2013 

AB installation 2012 - 2013 

L1/FPGA prototypes (8) 2010 - 2011 

L1/FPGA small series (32) 2011 - 2012 

L1/FPGA production 1 (96) 2012 - 2013 



L1/FPGA installation 2012 - 2015 

Table 10.2: Timetable of software and hardware needed for AB and L1/FPGA realization 

huge hardware units like the AB and L1/FPGA boards approximately one week is needed per three to 

six boards. The first quarter of the number of boards to build, of course takes more time than the second 

quarter, and the second half less than the first. 

To built 256 AB boards about two years are needed for production, test and commissioning. Before this 

one year for the production of a small series is needed and another year for prototypes. 

The technique of the L1/FPGA boards is very similar to the ABs, both consists of FPGAs, external 

memories and connections to the networks. Indeed less than half of the L1/FPGA processing resources 

need to be available at end 2013, but more L1/FPGAs are needed. The timetable for the L1/FPGA boards 

therefore is shifted by half a year. Because the number of L1/FPGAs is about double the number of ABs 

the production takes one additional year. L1/FPGA prototypes are produced at the same time with AB 

small series, and L1/FPGA small series is produced in parallel to AB production, and so on. In this way 

the L1/FPGA production can gain from the AB development. AB and L1/FPGA work packages can be 

done in parallel by adding manpower. On the other side the time for development and realization of one 

unit can not be accelerated by dividing the work packages to different teams, this would even have a 

worse effect because more communication between these teams would be needed. 

The R&D steps towards these prototypes are the following. In 2005 a hardware development is planned to 

test the physical layer of the multi gigabit communication. The emphasis of this boards are impedances, 

board layout, connectors, and the different multi gigabit connections via optics, copper or backplane, 

from 2.5 Gbit/s to 10 Gbit/s per link. In 2006 these tests of communication are enlarged to protocols 

and their implementation. In parallel to these communication issues the algorithms to be implemented 

in hardware have to be tested.


Another two board developments are scheduled for end 2006 and 2007 where the features of new devices 

and new FPGA families have to be evaluated. This boards are needed to fix the requirements and size of 

FPGAs and external memory as well as communication issues on the way to standard protocols and real 

implementation of algorithms. 

For these tests several small R&D prototypes with different numbers and sizes of FPGAs and external 

memory are needed. Beginning of 2005 two small systems with cost-efficient state-of-the art FPGAs 

are scheduled. At begin 2006 an update of this systems with components adapted to the requirements 

of the algorithms are planned, e.g. more FPGA resources and large memories. End 2006 and 2007 new 

R&D prototypes are build that use the up-to-date technology, that is more adapted to the needs of the 

final hardware processing units. The used FPGAs will be high-end devices and therefore look expensive 

during R&D, but they are expected to be middle-of-the-road FPGAs at 2012 to 2014. 

In 2008 first pre-prototypes of the hardware units are built to test the real algorithms and communication 

protocols. These units have already the size of the boards used later in the experiment and also the final 

control infrastructure. The development and test of these infrastructure is of course part of the R&D 

prototypes. 

The years 2008 and 2009 are the most critical in this timetable, because here the final decisions for the 

system have to be derived. The first version of the hardware programming software has to be ready at 

end 2009 and the hardware architecture, concerning the type of FPGA, external memory, network and 

protocol has to be done. At end of 2010 also the decision of the COTS components has to be fixed, this 

is t0 minus 3 years, what is a typical latest date for this decision. 

In 2011 already first prototype processing farms are available using the prototypes developed for the ABs 

and the pre-prototypes used for L1/FPGAs. The prototype COTS processors are also part of this farms, 

with an even higher percentage than in the later system. 

In 2012 already one sub-detector can be read-out and processed by about 8 sub-farms having about 1.5% 

of the final processing power. One year later about half of the detector can be readout and 12.5% of the 

compute resources are available. End of 2013 the full detector can be readout and 43% of the compute 

resources are available. This can be translated to a CBM event rate up to 4 MHz. The full processing 

power will be installed during 2014/15. 

System cost 

The following cost estimate is based on our experience in building complex real-time processing systems 

in hardware. Our assumption is, that a hardware unit that is able to process a 10 Gbit/s input data stream 

in real time will cost about 1 k, incl. board, power, crate, backplane, network and control. 

L1/FPGA, AB 

1024 × AB a 1k 1 M 

2048 × L1/FPGA a 1k 2 M 

Cost of R&D prototypes, prototypes 500 k 

and small series (spare for system) 

L1/CPU 

2048 × PC a 1k 2 M 

or 20000 × SoCs a 100 

Table 10.3: Estimation of system cost 

The total cost for 1024 ABs and the 2048 L1/FPGAs therefore is 3 M. The cost of the R&D prototypes, 

the prototypes and the small series of the hardware boards sums up to additionally 500 k.


The hardware cost of one of the 64 sub-farms is: 16 k for AB, 32 k for L1/FPGA, 32 k for L1/CPU. 

Gives a total of 80 k per sub-farm. 

10.4 Feature Extraction and Event Selection 

Typical central Au-Au collision in the CBM experiment will produce up to 1000 tracks in the inner 

tracker. Large track multiplicities together with the presence of a non-homogeneous magnetic field make 

the reconstruction of events complicated. Therefore the collaboration performs an extensive analysis of 

different methods of event reconstruction in order to better understand the geometry of the detector and 

to investigate specific features of accepted events: 

1. Hough Transform for track finding in STS; 

2. Cellular Automaton for track finding in STS and TRD; 

3. Kalman Filter for track fitting; 

4. Kalman Filter for geometrical and mass-constrained vertex fit; 

5. Elastic Net for standalone RICH ring recognition. 

Status and results of testing of the algorithms are presented in Chapter 13. 

The most demanding task concerning processing is filtering events with open-charm in the first level. 

D mesons are detected via their weak decay into charged pions and kaons. The difficulty of this measurement 

lies in the very low multiplicity of D mesons (about 10 −3 per central event) within an environment 

of about 1000 charged hadrons produced in such a collision. Evidently, the combinatorial background 

stemming from directly produced particles has to be suppressed by many orders of magnitude. The mean 

lifetime of the D 0 mesons is cτ = 124µm. The secondary vertex resolution depends on the material in 

the first detector stations, causing multiple scattering, and the intrinsic single-hit position resolution of 

the tracking detectors. 

The vertex resolution required for open-charm detection is about 50 µm. To achieve this, the resolution 

of the very first detector layers after the target (5 cm and 10 cm distance) must have a position resolution 

below 10 µm and a very low material budget. The MAPS technology is aimed to fulfill this requirements 

but has the disadvantage of the very slow readout speed of about 10 µs resulting in the accumulation of 

about 100 minimum bias events. 

To measure the momentum of a particle the bulk area of the tracking stations will be covered by silicon 

strip detectors with matched strip geometry. Detector layers will be at distances of 20 cm, 40 cm, 60 cm, 

80 cm and 100 cm. 

The transition radiation detectors (TRDs) are aimed to track and identify high energy electrons and 

positrons (γ > 2000). These particles are then used to reconstruct J/ψ mesons. Tracking will be done 

either standalone only, or merging TRD tracks with tracks found in STS to improve track parameters at 

the target region. 

In combination with ECAL and ToF measurements TRD tracks can be used also for particle identification 

in D mesons analysis. 

The track reconstruction out of the raw hit data from the detectors is the hardest part of the processing, 

because no data reduction can be done so far. All subsequent parts can take advantage of the results 

derived from the tracking, like an assumption of the momentum and charge of the particle. 

In this section we describe the filter strategy and some implementation scenarios for selected algorithms.

10.4. Feature Extraction and Event Selection 259 

10.4.1 Filter Strategy 

The process of event filtering can be broken down into several largely independent steps. Some of this 

steps have to be performed in real time by dedicated processors, others are better running on CPU or at 

off-line stage of data analysis. In the first step, the data of each detector are preprocessed already in the 

readout electronics (FEE) to make a compression of data, to minimize the bandwidth of the data transfer. 

The second step, the focus of this section, is a local pattern recognition within subdetectors, that will be 

the base of selecting interesting events. The third step consists of global track finding and fitting, making 

full event reconstruction in the setup. 

In the following we will describe two approaches for the most demanding task, the selection of events 

containing decays of D-mesons. One of them is more hardware oriented to be implemented in a first 

filter level. The other is more software oriented to achieve a more accurate and precise processing in the 

second filter level. 

In order to filter out decays of D-mesons the data of the silicon vertex detector (STS) is used to track the 

particle traces. Special high resolution detectors (MAPS) are used for the determination of the primary 

and secondary vertices. Finally a decision whether the event is selcted or not, has to be generated 

according to the invariant mass of particles coming from a secondary vertex, using assumed particle 

identity. 

Here is an overview of the filter strategy and the used algorithms. Details of the concrete algorithms are 

given in Chapter 13. 

• Tracking of STS data. 

Tracking algorithms can be divided into local and global methods. Local methods process the 

tracks independent from each other. Some few points generate an initial track candidate. Using 

interpolation or extrapolation additional points are collected. When more points can be found the 

track candidate is assumed to be good, otherwise the track is discarded. A method is global when 

all points are processed by the algorithm in the same way. The algorithm produces a list of tracks 

or a list where tracks can be found easier than in the original data. Global methods can be seen as 

transformation. 

We have implemented one of the well-known global methods, the Hough transform, in order to 

investigate its hardware (e.g. FPGA) applicability for a first filter level. The Hough transform uses 

a parametric description of a track by a set of its parameters. Once the track model and detector 

measurement model are given, all hits in the detector can be projected into the track parameter 

space creating a complex density distribution (histogram) with local maxima. In this case the track 

recognition becomes a search for local maxima corresponding to tracks. 

A very fast software implementation of a local method is a cellular automaton algorithm. The cellular 

automaton method creates short track segments in neighbored detector planes and links them 

into tracks. Being essentially local and parallel cellular automata avoid exhaustive combinatorial 

searches. Since cellular automata operate with highly structured information, the amount of data 

to be processed in the course of the track search is significantly reduced. Cellular automata employ 

a very simple track model which leads to utmost computational simplicity and a fast algorithm. 

• Track following through the MAPS detector. 

The readout time of a MAPS detector is in the order of 10 µs, that is a relatively high time period 

compared with the about 10 MHz event rate. This leads to an event pileup of approximately 

100 events in this detector type. The only feasible way to follow a track through this bulk of accumulated 

multi event data is to expand the fitting of the found tracks from the STS tracking into 

this detector.


The Kalman filter is an algorithm to calculate an optimal estimation of a value based on any 

kind of measurements taking into acount also their accuracy (e.g. detector resolution and multiple 

scattering). Based on a measurement it updates its estimate and generates a new more precise 

prediction of the value. 

The Kalman filter is implemented with different emphasis, one is optimized for processing speed, 

the other for precision. 

• Primary and secondary vertex determination. 

The determination of the primary vertex is done by expanding the Kalman filter process through 

the MAPS detector to the approximate vertex position. 

All particle tracks that do not intersect this primary vertex have to be processed once again. For 

all combinations of tracks the nearest points of intersection (χ 2 ) have to be calculated. If there is a 

near intersection this point is a possible secondary vertex and the invariant mass of this tracks with 

an approximate momentum from the tracking and an assumed particle identity is calculated. 

For J/ψ event selection the procedure is following: 

1. tracking of TRD data; 

2. select electrons and positrons; 

3. roughly estimate their momenta from deflection angle in the magnetic field; 

4. select electron-positron pairs with invariant mass close to the J/ψ mass; 

5. propagate tracks of these candidates to STS; 

6. merge them with found STS tracks or use them as seeds to find tracks in STS; 

7. improve track parameters in the target region; 

8. calculate invariant mass. 

J/ψ event selection can use measurements of other detectors if necessary. 

Fig. 10.11 gives an overview of the algorithms and their association during event processing. 

10.4.2 Implementation Scenarios for Selected Algorithms 

In this section we describe an assumption of the requirements for implementation and processing for a 

selection of concrete algorithms. 

Aiming their implementation in all levels of event selection the algorithms are being developed in two 

complementary directions: from the high level in their base form to the hardware implementation in 

L1/FPGA and vice versa. Such strategy allows to investigate both efficiency and applicability of the 

algorithms. The algorithms in their base level form are described in the chapter concerning physics 

performance. 

Here we give short description of the Hough transform method as it can be implemented in L1/FPGA. 

The Hough transform is aimed for a direct implementation in hardware to provide a very fast tracking 

for the first filter level. A very simplified version of a Kalman filter is shown together with a possible 

hardware implementation.


Event Processing Steps 

STS Data 

HT/CA Track Finder 

KF Track Fit 

PV Finder 

PV GeoFit 

Performance 

RICH Data 

EN Ring Finder 

Track Merger 

KF Track Fit 

SV GeoFit 

SV ConstrFit 

Select/Discard Event 

TRD Data 

CA Track Finder 

KF Track Fit 

09 Dec 2004, Hd Ivan Kisel, KIP, Uni-Heidelberg 

Uni Heidelberg 6 

10.4.2.1 Hough Transform 

Figure 10.11: Event processing steps 

The Hough transform is a global tracking algorithm and therefore its number of operations is proportional 

to the number of hits to process. It has the advantage that it is robust against additional or missing noisy 

detector hits and also sensitiv to ill-defined objects. A detailed description of the algorithm and the 

physical performance is given in 13.1.1. Here we describe a possible implementation of the Hough 

transform using Field Programmable Gate Array (FPGA) and lookup tables (LUT), like used for the 

hardware processing units (L1/FPGA) of the first level farm (see 10.3.2). 

The detector hits with the coordinates x, y, z are transformed in a parameter space according to 1/Pz, 

Px/Pz and Py/Pz. It is divided of several 2-dimensional Hough transforms where each detector slice 

(red area in detector in fig. 10.12) contributes to a certain plane (red area in Hough histogram) in the 3dimensional 

Hough histogram. In the first step the hits are stored in a buffer according their y/z-position 

using a LUT. Hits with different y/z are processed subsequent by calculating a 2-dim. Hough transform 

for each y/z value. A second LUT determines the shape of the curve in the Hough space generated by a 

hit. The curves of all hits are accumulated in a Hough histogram. A peak determines a found track and 

its momentum parameters 1/Pz, Px/Pz, Py/Pz. 

The goal is to process one detector hit per clock cycle. A 10 Gbit/s input link is able to transfer 150 · 10 6 

hits/s, expecting a hit is coded with 64 bit. The necessary frequency for processing therefore is 150 

MHz. The processing time depends only on the number of hits in an event. With 1,500 to 10,000 hits per 

event a rough estimate of the processing time is between 10µs to 100µs. As each 10 Gbit/s link can be 

processed in real-time the number of needed Hough units is about the same as detector data links from 

the STS are needed, that are approximately 200. 

To process one detector hit in one clock cycle the complicated calculations of the Hough transform 

according to the real detector geometry and the magnetic field are implemented with LUTs. Using LUTs 

also inhomogeneities of the magnetic field can be considered and put into calculation. 

The Hough transform consists of two parts (see fig. 10.12). First the y-z projection of a track as a straight 

line applied. As described earlier the 3-dimensional Hough histogram is decomposed into several 2dimensional 

Hough histograms. In principle this decomposition can be calculated using the y and z 

coordinate of a hit. In a first LUT the numbers of the 2-dimensional Hough-histograms are stored where


Y 

X 

Z 

Py/Pz 

Px/Pz 

from Active Buffer 

buffer 

x,y,z 

x,z 

LUT 

y,z 

LUT 

Hough-histogram 

1/Pz 

peak finding 

Px/Pz 

1/Pz, Px/Pz, Py/Pz 

Figure 10.12: 3-dimensional Hough transform and hardware implementation. 

a hit contributes. The output of this LUT is used to store the hits according to this 2-dimensional Hough 

planes. 

In the second step the 2 dimensional Hough histograms are calculated. This can be done subsequent or 

in parallel. A second LUT is accessed using the x and z coordinate of a hit and it outputs the coded shape 

of the transformed curve in the Hough space. The slope of this curve is dependant on the z coordinate, 

the start value is dependant on the x coordinate of the hit. 

The accumulation of the Hough curves to a histogram is done in a systolic array of simple processing 

elements (see fig. 10.13). Each element consists of a counter to store the histogram value, a flip flop for 

the short-term storage of the Hough curve, and a multiplexer to select from two neighboring elements 

to determine the shape. In every clock cycle a new start value is input to the array and the appropriate 

element in the first row is set. In the next cycle, in parallel to the setting of a new start bit in the first row, 

the former bit is transferred to the directly neighbored element or the diagonal element in the second row, 

and so on. The multiplexers of the elements of one row to select the particular input are controlled by 

one bit per row, that are delayed by shift registers. The slope of a curve can be choosen arbitrary between 

the minimum and maximum slope of 45 and 90 degree. 

The counter of each cell of the histogram stores the number of hits from the different detector layers that 

lay on a trajectory of a track with the associated parameters. The counter not just counts the number of 

hits, but each hit of a certain detector layer sets a certain bit in the cell. At the end it can be seen which 

layer has contributed to the track candidate. Clusters or nearby hits are counted as one hit (with a larger 

size), but do not inflate the counter value. When all bits of all layers are set, there is at least one hit in 

every detector layer. So the track candidate is accepted as a found track with the parameters determined 

by the cell position. It also happens that tracks do not pass all detector layers or that hits are missing due 

to detector inefficiencies. Acceptable tracks should come from the vertex, there has to be one hit in the


x 

LUT 

detector 

hit coordinates 

x, z 

1 bit/row 

z 

shift registers 

start 

Figure 10.13: Implementation of a Hough transform unit as a systolic array of simple processing elements. Each 

processing element consists of a counter to store the histogram value, a flip flop for the short-term storage of the 

Hough curve, and a multiplexer to select from two neighboring elements to determine the shape. The array is 

controlled by a start value and a 1 bit/row control word. 

first layer (STS3), and there should be at least two other hits in the layers STS4 to STS7. Other rules for 

the acceptance of a track in dependance of the different counter states are programmable. 

An efficient implementation as a systolic array within a FPGA was elaborated. To implement a 2dimensional 

Hough histogram with 31 × 93 cells together with the peak finding unit and the units to 

control the LUTs and memories, about 35.000 to 40.000 logic cells are needed, what fits into a mid-size 

to large state-of-the-art FPGA. About 8×(1M ×16) SRAMs as external memory are needed for the large 

LUTs and the hit buffer. 

10.4.2.2 Fast Kalman Filter Hardware 

The Kalman filter is an algorithm to calculate an optimal estimation of a value based on any kind of 

measurement taking into account also their accuracy. Based on a measurement it updates its estimate 

and generates a new more precise prediction of the value. 

In our case the prediction of the position of a track hit in a previous detector layer was calculated by 

assuming a straight line in the non-bending y-z plane and a parabola in the magnetic x-z plane of the 

detector. The Kalman filter algorithm was simplified in terms of complexity as well as accuracy of 

the calculations, e.g the noise and error covariance was chosen to believe the latest measurement. The 

measurement nearest to the target has the lowest influence of multiple scattering because it has passed 

the fewest detector planes. As the calculations are aimed to be processed directly in the parallel hardware 

of a FPGA, the coefficients and parameters of the Kalman filter are presented as integer values with 10 

CNT 

D Q


to 12 bits resolution. Prior to the calculations these values have to be mapped to a new number range. 

The calculation itself then are mostly simple additions where the result once again has to be converted 

to the new range of the result value. The LUTs to convert the ranges of the values are comparable small 

because only 10 to 12 addresses are needed. They may well be implemented using the internal resources 

of a FPGA. Also adders can be implemented efficiently in FPGAs. 

The hardware challenge is the realization of the detector hit memory. It is addressed by the predicted 

position of a hit and has to output the relative position of the nearest hit stored in the memory. This is 

the functionality of an associative memory. In order to store the accumulated data of ca. 100 events 

according to the geometry and position of a hit, several tenth of Mbit are needed. 

The Kalman filter implementation works very efficient. Simulations of the described algorithm with 

Monte Carlo data showed that without pileup about 98 % of the nearest hits were from the same track. 

With a pileup of 100 events the performance drops by about 10 %. This means that for about 12 % of the 

tracks the nearest hit to the prediction was not from the same track but from a track from an event that 

was piled up in the detector. The efficiency shows a strong dependency upon the momentum of a track. 

For tracks with a momentum P < 1 GeV/c the efficiency very fast is much smaller than 80 %. For tracks 

with a momentum P > 1 GeV/c the efficiency is better than 90 %. 

The processing time for the Kalman filter track following through the MAPS detectors is proportional to 

the number of tracks that have to be processed. 

Primary Vertex Determination 

The primary vertex is identified by calculating the z coordinate of the points of intersection in the nonbending 

y-z plane. To save processing power an intersection of a found particle track is calculated not 

with all tracks, but only with about max. 100 previously stored tracks with opposite charge (sign of 

bending found by the tracking). To determine whether a track is coming from the primary vertex the 

z coordinates of the intersections are histogrammed. If one of the calculated z coordinates is equal to 

the peak of the histogram (the position of the primary vertex) it is flagged as "from primary vertex". 

A calculation of the x-position at the vertex position delivers additional accuracy for the origin of the 

particle. 

First simulations of this algorithm showed that only about 5 % of the tracks found by the tracking remain, 

that are most probably from a secondary vertex. The position of these possible secondary vertices has to 

be calculated in a next processing step. 

A track can be described by parameters coded in less than 100 bit. The memory needed for the storage 

of the 2 × 100 tracks therefore is very low (totally ca. 20 kbit). 

The calculations needed for the determination of the point of intersection is comparable with the complexity 

needed for the update step of the Kalman filter for the linear y-z plane. The parameters of the 

tracks are coded in the same way as they are used and output from the Kalman filter. In this way an 

efficient implementation with FPGA is possible, using adders and internal memory for the LUTs. In 

order to process as many as possible of the up to hundred calculations of the point of intersection in 

parallel, many parallel units within one FPGA device are used. Taking advantage of this parallelism the 

processing time for this step is in the order of one cycle per track. 

10.5 Controls (ECS) 

The Experiment Control System (ECS) has to ensure the coherent and safe operation of the CBM experiment. 

One main requirement is the easy integration of independently developed components.

10.5. Controls (ECS) 265 

10.5.1 General Description 

The Experiment Control System is used to supervise and operate all detector hardware, the common 

experimental infrastructure and it provides the status of detector components for the CBM DAQ. It interfaces 

to the GSI infrastructure and to the SIS accelerator. 

The ECS does not deal with the safety of personnel. 

10.5.2 Detailed requirements 

The ECS is capable of operating the detector hardware without the DAQ system. Therefore it should 

wherever possible use its own control path to the hardware. 

The ECS is build as a client-server model. The clients are the distributed user interfaces (GUI), archiving 

stations, alarm handlers, and other high-level interfaces. The servers are agents running either in the DCS 

boards or workstations or single board computers which interface to the specific detector or infrastructure 

hardware. 

Clients and servers communicate over IP networks with protocols like channel access (CA), DIM or 

similar. 

The ECS can be partitioned in sub-detectors for development and separate testing on a per agent basis. 

The ECS has to integrate independently developed components by providing interoperability between a 

small number of allowed standards (field busses, SCADA systems, common hardware) 

10.5.2.1 Functionality 

The ECS provides for setting the detector system into a runnable status by loading parameters and configuration 

files from the database to detector and trigger hardware. For speed of operation the database 

will be distributed and local caches in agents or intermediate management layers will be used (LAM and 

WAM). 

The ECS monitors operational parameters from detectors, trigger hardware, high and low voltage supplies, 

gas systems, position measurement systems, and environment and provides a GUI to these process 

variables (PV) to the user. 

All relevant process variables are archived to the database. Historical views and trending is provided to 

the user and via APIs to the analysis software. 

The state of critical process variables is constantly monitored and compared against predefined boundaries. 

The Alarm handling client alerts the operator and logs alarm states and acknowledgments to log 

files (or database). 

Interlocks and detector safety are provided with dedicated hardware (i.e. PLCs), but the ECS monitors 

this hardware and the interlock status. 

Access Control to modification of parameters in the ECS and in the database are provided through user 

accounts and passwords.


10.5.3 Possible Implementation 

10.5.3.1 Connections 

It is advisable to use different paths for ECS and DAQ in order to guarantee status messages and state 

transitions to be received regardless of the state of the DAQ. The different paths can be provided in two 

ways, either by a completely separated network or by using extra fiber bundles of the same type as used 

for the DAQ. In this case it has to be guaranteed that the connecting network infrastructure (switches) 

can not be blocked. There has to be the possibility for all components in the ECS and DAQ to switch on 

and off remotely, either successively by the control network or by a separate power distribution system. 

10.5.3.2 Software 

For the reason of maintainability and licensing costs we want to use wherever possible open source 

software. There are several packages which are used with good experience in the HEP community, like 

EPICS and DIM. Some of the collaborators already have used these packages in previous experiments 

and can build on their knowledge. One big advantage of these packages is that one can influence the 

development of this software oneself by contributing new solutions for new hardware or interfaces. 

On the other hand there are many laboratory style hardware setups like gas systems, high voltage supplies, 

connections to PLCs, which are build around commercial SCADA packages like LabView. Of 

course we will try to interconnect these sub systems to the general ECS by writing gateways. 

10.5.3.3 Hardware 

The hardware which will be supervised by the ECS fall in two categories: 

• Standard Hardware 

• The DCS board 

Standard Hardware 

Early on a set of standardized hardware components which can be reused in the different detector sections 

has to be identified, as there are: 

• High Voltage 

• Low voltage power supplies 

• Crates and racks 

• Temperature sensors 

• Position sensors (Critical for STS tracking stations) 

• Gas supply systems, valves, flow meters 

Through the standardization we will achieve fewer different components, more reusable parts in hardware 

(spares) and software and bigger discounts in the acquirement.


10.5.4 The Detector Control System (DCS) 

The detector control system (DCS) is an agent unit for many detector front-end components. It acts as a 

status and event server for the ECS system as well as a client for controlling all front-end electronics. 

The DCS will be implemented as a single board computer 2 . It consists of DCS hardware (DCS board), 

an operating system and the DCS application. 

10.5.4.1 Functional Description 

The main tasks of DCS are to securely switch on and off front-end electronics, to configure all components 

(including FPGAs), and to set all software and hardware parameters. Additionally the DCS should 

implement the runtime refresh for all FPGAs exposed to high radiation. 

DCS generates control events and passes them to the experiment control system (ECS), and finally it 

produces all necessary information for generating the detector status in the ECS. 

The basic principle is to watch and control all necessary detector parameters like temperature, chamber 

gas pressure, supply voltages and many others. Therefore, the DCS needs to generate and pass control 

events if anything goes wrong. 

10.5.4.2 Implementation Issues 

The central processing element should be implemented on an FPGA due to the ability to configure DCS 

components during runtime of the experiment. The processor may either be a hard processor core (like 

PPC (Xilinx Virtex 4) or a soft core if its performance is acceptable. In 2012 the cost of a sufficient 

FPGA will be in the same scale as a microcontroller. Therefore FPGAs will become a powerful and 

highly flexible substitution to standard microcontrollers. 

The field bus between DCS and ECS should be a TCP/IP based protocol since this is a robust standard 

implementation based on a well known technology. It is not yet decided to implement it copper based or 

to use fibre optics instead. 

For flexibility and simplicity reasons, we propose to run embedded Linux on the DCU boards, since the 

application can run as a Linux process, and all infrastructure like TCP/IP stack, process management, 

mounting external file systems etc. is inherently available. 

10.5.4.3 DCS Operation 

The DCS watches four different types of parameters: 

• Threshold parameters need be set and verified during normal operation 

• Non critical parameters which will be read out from time to time like event counters 

• Switches need to be set like power on and power off 

• Parameter values like the chamber temperature or the voltage of the power supplies which need to 

be watched and will generate an error event if a threshold will be hit. 

2 It is therefore sometimes referred as DCS-board for clarity


The concept of control software is decentralized which means that the DCS tries to solve minor problems 

by its own. As long as everything is fine, a good-signal will be sent to the DCS either automatically or 

upon ECS request. This good signal may contain additional information like event counts etc. As soon 

as this signal is not received within a specified time, the ECS expects an error of the corresponding DCS 

and tries to reboot it. 

If a threshold value is missed, a control event will be generated. Currently we differentiate four priorities 

of control events: 

a) immediate event: a critical value was significantly violated. Therefore we need an immediate 

reaction like emergency shut off. 

b) critical event: A critical threshold value was reached. 

c) warning event: A critical value comes close to a threshold . 

d) info event: The DCU was able to resolve a problem by its own. This is an information to the DCC 

DCS also acts as a client for ECS commands: It should react on events like load configuration, configure 

FEE components, send status report, and switch on and off of the FEE power supply. 

10.5.4.4 Research Work 

A similar DCS system was build for the ALICE readout electronics. Therefore we expect some new 

ideas when this system will be used to control the ALICE Experiment. We assume that major parts of 

the ALICE DCS may be reused for the CBM detector. 

The new concepts include radiation tolerance (see chapter fault tolerance), partial runtime reconfiguration 

of the front-end electronics if FPGAs are used, and new parameters needed to be watched. 

Additional research work includes making the DCS a fully distributed, fault tolerant system. Therefore 

we need to ensure that no critical events will be lost by introducing redundancy. 

10.5.4.5 The ALICE DCS Board 

An appropriate device was build for the ALICE experiment, which may serve as a starting point for further 

considerations. The ALICE DCS board has a very high degree of flexibility, which is accomplished 

by using Alteras Excalibur FPGA with embedded ARM processor core on which the Linux operating 

system is running. In addition, the integrated, programmable FPGA allows the integration of specific 

functionality in a very flexible way and is being used to custom fit the device to the specific requirements 

of the various detectors. It is now being used by most ALICE detectors. The FPGA configuration and the 

program code for the CPU is stored in a Flash ROM from which the system is automatically configured 

after power-up. An ADC for digitizing analog signals coming from the detector, a SDRAM chip, and 

a FastEthernet PHY transceiver chip complete the system. Full control over all on-board configurable 

components, such as the FLASH memory or the FPGA, is ensured and mutual reconfiguration of the 

boards in case of failure is feasible with a JTAG chain. 

The ARM processor runs Linux as operating system, therefore simplifying the development of application 

software since the well known GNU standard tools (compiler, debugger, and libraries) can be used. 

For instance, a web server was successfully ported and operated on the board. This allows for a very 

comfortable configuration and monitoring scheme of individual detectors, based on the http protocol 

together with the common gateway interface (CGI) using any web browser in combination with the onboard 

web server. The high level control and monitoring functionality of the detector control system is


Figure 10.14: A photo of the 

ALICE DCS front-end configurable, 

low cost single board linux computer, 

running Ethernet as fieldbus, 

operable in magnetic fields. 

based on the Distributed Information Management System (DIM). This requires a so-called DIM-server 

running on the DCS single board computer. This server is responsible for publishing measured slowcontrol 

data to the next higher instances in the hierarchical DCS system. Commands to the DCS single 

board computer are also sent to the server. An appropriate DIM server was ported and is operational 

on the first prototypes of the device. All programs necessary for starting the system and its operation 

are permanently stored in the Flash ROM. Therefore, the DCS single board computer can be operated 

stand-alone and it does not require a network connection in order to perform the basic control tasks. 

This feature is especially important in terms of safety requirements where a proper and secure operation 

of the detector has to be guaranteed even in cases where part of the higher levels of the control system 

are not working. During operation the Ethernet connection is required only for publishing measured 

data. It is also used in spy mode to access on-line data without affecting the main detectors data stream. 

This feature is very useful in particular during the debugging phase of the detectors and even during test 

measurements where performance is not essential. 

The communication with the DCS board requires a reliable field bus. One possible network technology 

which is discussed very actively in industry at the moment is Ethernet as field bus. One advantage of 

using Ethernet is its wide use and therefore the availability of cheap standard components. However, 

the proper function of Ethernet even in a harsh environment with electromagnetic background noise has 

to be ensured. Another complication arises due to the presence of strong magnetic fields since standard 

network devices use a transformer for electrical signal decoupling. In case of the DCS board this problem 

is solved by replacing the transformer with a small amplifier circuit which boosts the output signal of the 

physical layer chip. Measurements were done with a setup consisting of a DCS single board computer 

and a standard Ethernet switch with 50 m UTP CAT5 cable between the two devices and verified the 

proper function of the modification. 

Although different commercial network interfaces for Ethernet exist, a different approach was chosen 

for the DCS board. The Medium Access Controller (MAC) of the DCS board is realized as a very 

lightweight synthesizable module in VHDL, called Easynet. By the renouncement of collision detection 

this device uses only a minor fraction of the FPGA resources. Besides the benefit of eliminating 

external chips this approach allows in addition to complement the existing Ethernet protocol with quality 

of service functionality, allowing for the reservation of bandwidth and implementing high priority, 

reliable messages. Such functionality can be integrated and operated with existing commercial network 

components provided that all participating MACs adhere to such functionality. 

Figure 10.14 shows a photo of the low cost DCS board as it is used in the detector control system of 

ALICE at the LHC. The board will be used for example in the TRD, TPC, and PHOS subdetectors 

and will be responsible for monitoring and control of the overall working conditions of these detectors 

like voltages, temperatures, and pressures. Furthermore, it can be used as a test read out system of the 

detectors. This functionality already proved very useful during tests and conditioning of the ALICE TRD


detector. The system is able to react autonomously to critical situations in order to avoid damages of the 

detectors. In addition, the board is used to initialize and configure the front end electronics. Table 10.4 

summarizes the overall features of the board. 

CPU ARM922T, 32-bit RISC 

FPGA 100 000 gates (1 000 000 optional) 

4 160 Logic elements (38 400 optional) 

SDRAM 32 MB 

Flash memory (ROM) 4 MB (8 MB optional) 

ADC 8 channel, 16-bit 

Power consumption < 4 W 

Table 10.4: Key parameters of the ALICE DCS single board computer. 

10.5.5 Configurations and Update Management 

The Configurations and Update Management system is part of ECS software. Its key feature is the ability 

to handle numerous different configurations of all parts of the electronics. These configurations include 

detector test procedures as well as different scenarios during lifetime of the experiment. 

In the case of a large detector, a configuration and update management system needs to manage FPGA 

hardware configurations, software applications, and all parameters of the front-end electronics. 

For the optimal use of limited storage possibilities on detector control devices and the immense amount 

of data to be saved on a server or to be distributed to the detector control devices, it is necessary to 

develop a new strategy for handling the data. 

10.5.5.1 Functional Description 

Due to the large number of processed detector configurations and hardware, software or parameter updates, 

a fully decentralized system architecture is required. The decentralized character of the architecture 

is based on the idea to transfer some of the management tasks into the device and thereby reduce 

communication efforts and to process configurations as close to the device as possible. 

The management environment should be redundant to guarantee highest availability from configurations 

databases and servers. Redundancy is also required for having access to additional hardware devices for 

solving problems during a reconfiguration or update procedure (disaster recovery). 

The configuration and update system has to guarantee a fast configuration and update process since the 

detector will be unavailable during a setup or update. This also holds for configuration repository: it is 

important to develop a new method for a decentralized storage of configurations and updates and new 

algorithms to get the device status with a minimum of communication. 

Finally the monitoring of the configuration and management process is necessary to decide whether the 

configuration or update was completed successfully. In case of failure the management unit is able to 

generate an event for notifying the experiment control system. 

10.5.5.2 Implementation Issues 

We propose a multiple level decentralized client server architecture. The architecture is based on a very 

scalable multi-level server network.

10.6. Computing 271 

The backbones of this network are so called Local Area Management (LAM) servers. A LAM is responsible 

for a set of detector control units (DCU). The LAM has the competency to manage and store the 

configuration and updates releases and to determine which one should be deployed for a device. 

The DCUs are the last devices with the functionality to communicate with the management servers. The 

end devices could be configured or updated by the DCU, e.g. they provide the necessary features for 

configuring devices that can not or should not directly communicate over the network protocol. On top 

of the LAM servers are one or more layers of Wide Area Management (WAM) servers. They extended 

the functionality of LAMs for coordination and synchronization tasks. 

To develop an efficient methodology for configuration and update management it is important to offer 

a facility to describe relationships between device classes and configurations of them. Due to these 

demands we decided to use the object oriented UML technology. 

Both configurations and updates are described and modelled by an UML scheme. Using the UML input, 

the management system will be able to automatically synthesize instances of the classes. These instances 

are described using XML. The XML coded data contain all parameters needed to communicate between 

management system and managed devices. These files could contain both information about a current 

configuration of a device and binary code to update a device. 

The information gain of the XML Documents will be saved in a decentralized database located in all 

servers through the architecture. Using the database the management system will be enabled to supervise 

the configurations states of all managed devices. 

10.5.5.3 Research Work 

A preliminary prototype system was already developed to show proof of concept. Additional research is 

necessary for implementing the full data flow. 

The focus of research work is 

• A new algorithm for the decentralized management of configurations and update releases. 

• An algorithm to recover the last working configuration in case of errors 

• Test and performance evaluation 

10.6 Computing 

The last stage of the readout chain consists of a compute farm for processing complete events in realtime. 

This High Level Processing System (HLPS) will be built with commodity processing elements 

connected by a commodity network. While technical implementations cannot be predicted at this point 

of time, the physics requirements are well defined and concepts for processing models can be discussed. 

10.6.1 Physics requirements 

The accelerator complex will deliver beams of heavy ions and protons to the experiment for about 

5·10 6 seconds per year, stretched over 4-5 months (see section 11). Four main physics programs are 

envisioned at the moment; two programs for the measurement of rare signals (J/ψ and open charm), 

which have such a distinct decay topology that the event selection system described in section 10.4 can 

reject background events. The other observables are either indistinguishable from the background on an 

individual basis (low-mass di-lepton pairs) or abundant enough in a minimum bias event sample.


• J/ψ 

A signal rate of 0.3 Hz is expected at an interaction rate of 10 MHz for Au+Au collisions. The 

irreducible background rate in the current experimental setup is 50 Hz which is negligible compared 

to the bandwidth of the archiving system - assuming an efficient event selection scheme. 

The estimated run time is one year of Au+Au, one year of A+A and several months of p+p (see 

section 11). 

• Open charm 

A signal rate of 0.6 Hz is expected at an interaction rate of 10 MHz for Au+Au collisions. A 

reduction of the background rate to less than the maximum output of the DAQ (25 kHz) seems 

achievable. This program can run in parallel to the J/ψ program. 

• Low-mass di-lepton pairs 

No event selection scheme is applicable here because the signal cannot be uniquely identified 

in an individual event but has to be deduced from ensembles. Therefore, the interaction rate 

cannot exceed the maximum input rate into the high level processing farm and/or the output to the 

permanent storage (25 kHz), yielding a signal rate of 0.5 Hz. The expected run time is 36 weeks 

for A+A collisions and 30 weeks for p+p and p+A interactions. 

• Hyperons 

The hyperon program is a subset of the low-mass di-lepton pair run. 

10.6.2 Event structure 

The multiplicity of charged primary particles produced in a central Au+Au collision at 25 AGeV is 900, 

out of which 600 charged tracks will be detected (and in addition gammas and neutrons in the ECAL). 

The data structure in the front-end is dictated by the self-triggered electronics and the push architecture 

and is portioned into time slices which may contain one or up to 100 events. We assume that the frontend 

electronics and level-1 event selection will reduce the raw data information - consisting of clusters 

of ADC counts, characterized by time stamps and organized in time slices - into single event structures 

containing hit information. 

10.6.2.1 Event size 

The main tracking device is the Silicon Tracker System (STS) consisting of 3 layers inner tracker (ITS) 

and 4 silicon strip layers. 600 charged tracks will create 4200 hits, beam deltas will add another 300 

hits of incoherent background in the ITS. After the first levels of processing, only hit coordinates, charge 

sums and tracking parameters for each event will be passed on to high level processing. The data volume 

is estimated to be 17 kbyte ITS data, 20 kbyte for the strips and 60 kbyte of track information. 

The occupancy in the RICH detector is assumed to be 10%, resulting in a data volume per event of about 

10 kbyte. 

The TRD consists of three stations with 12 planes in total. Assuming local pattern recognition and data 

reduction, the event size into the high level system is about 40 kbyte of hit information and 60 kbyte of 

tracking parameters; amounting to 100 kbyte. In addition to these hits, we expect secondary tracks to 

contribute to the data volume by a similar amount of hits, so that the total volume increases to about 

200 kbyte. 

The ECAL adds about 20 kbyte (assuming 25% occupancy and local tower analysis), the RPC produces 

about 8 kbyte.

10.6. Computing 273 

The total event size of a central collision - processed hits, tracking parameters etc. - is therefore 

335 kbyte. Running at 25 kHz with minimum bias events (the average event size is about 1/4 of a central 

collision) will result in a data rate of about 2.1 Gbyte/s into the high level processing system. The lowlevel 

event selection will certainly bias the data sample towards more central collisions, so the data rate 

could be as high as 4.2 Gbyte/s. 

After reconstruction and physics analysis the HLPS adds the event summary to the data stream (see the 

following paragraphs). Some of the intermediate information may be dropped or compressed, so the final 

data rate to be archived is estimated at 4.6 Gbyte/s. 

10.6.2.2 Event hierarchy 

The interdependencies between the various detector data determine the hierarchy of processing steps and 

the degree of parallelization. The STS provides the primary vertex position, track impact parameters, 

primary track momenta, secondary vertices and secondary track momenta. The TRD provides track 

trajectories (straight lines) which can be mapped into track momenta for primary tracks. Together with 

the charge information, the TRD can identify high momentum electrons by itself. Matching these tracks 

with the ones in the STS would improve the momentum resolution and allow to distinguish secondary 

from primary tracks. The RICH provides PID but needs track trajectories and track momenta (taken 

from the STS). The same holds true for the RPC (TRD tracks or STS + TRD tracks). The ECAL finally 

provides information on energy, position and PID. 

10.6.3 High level event reconstruction 

The main steps in the event reconstruction are the TRD tracking and momentum fitting, track fitting and 

primary and secondary vertex finding in the STS and a global trajectory fit (STS, TRD and hits and the 

PID detectors), followed by an analysis of the detectors providing PID. Two processing scenarios will be 

pursued: 

• Flat scenario: Starting from the raw data a full event is reconstructed at once. Results from pattern 

recognition at the lower levels may be used as seeds. 

• Hierarchical scenario: The results of the independent sub-event reconstruction at the lower levels 

are used as input to the global reconstruction, which combines the various processed detector 

information. 

The hierarchical approach takes advantage of the event reconstruction steps performed partially in hardware 

and avoids the duplication of the processing of the identical raw data fragments. However, this may 

result in a slight loss of efficiency and/or resolution if the detector response and the low level pattern 

recognition is not fully understood. 

Besides the creation of Event Summary Data (ESD) as the result of a full event reconstruction, the 

High-Level Processing System provides an online analysis of the physics observables like weak decay 

topologies and invariant mass spectra of leptons pairs (low and high mass) and open charm. The size of 

the ESD is typically 5% - 10% of the raw data. This extended ESD will be passed on to the permanent 

archiving system optionally together with the compressed (e.g. track model + packed residuals) detector 

data.


10.6.4 Estimate of computing power and storage 

The total amount of data (raw data and processed information) accumulated in a year of running is 

23 Pbyte if we assume Au+Au collisions. Approximately half of the time proton-induced reactions and 

lighter systems will be studied. Even if the event size is smaller (about 10% in the best case), the decrease 

in event size might be compensated for by an increase in rate. 

At the beginning of 2005, 10 Tbyte of fast disks will cost about 25 k, including disk servers. The 

harddrive areal density has been increasing by a factor two during the last years [201]. However the 

capacity per volume will not scale with that rate as the performance of the I/O interfaces does not scale 

at similar rates. The very visible trend is the use of smaller volumes. As a consequence the cost per 

bit is decreasing at the slower rate of 1.37 to 1.5 per year. Obviously there are many different trends 

conceivable. Therefore any assumptions made for the year 2014 may easily have an error of a factor 2. 

Today’s costs for slow mass storage (tape) is less: about 2.5 k for 10 TByte, but in view of the data rate 

of several Gbytes/s a tape solution will probably not be very applicable. For instance there is significant 

hidden cost associated with magnetic tapes, such as the requirement to reprocess them periodically and 

their storage. The disk mass store will not require backup schemes, as there are low overhead techniques 

to protect against the loss of data without ternary storage. Finally there are many other emerging permanent 

storage techniques, such as organic memories or holographic memories. They will be monitored 

carefully during the development of the CBM Experiment. 

It should be noted in this context that there is a large variety of advanced data compression techniques, 

taking into account the physics signature of the data. It has been shown that standard lossless compression 

schemes, e.g. entropy coding, can reduce the raw data volume to 50% [202], while advanced 

techniques like e.g. track and cluster modelling are able to compress the raw data volume of tracking 

detectors by a factor of 10 and more [203]. Such methods assume a good knowledge of the detector performance 

and will therefore be not very efficient in the first year of data taking, but the experiment will 

not run at full luminosity and read-out rate in the beginning anyhow. Assuming that the CBM-detector 

data can be compressed by such advanced methods by a factor of 5 and that only this information is 

stored together with the extended ESDs, the demand on the mass storage shrinks to about 5 Pbyte/year. 

Timing measurements of processing data from existing detector systems and from detailed simulations 

for future experiments are used to estimate the computing power needed for the full reconstruction of 

CBM-events. The NA49 experiment has recorded central Au+Au collisions at 30 AGeV and its acceptance 

roughly matches the one of CBM, but the number of space points per track is much larger (about 

a factor of 10). The reconstruction is not optimized for speed. It takes approx. 4 minutes to reconstruct 

a central event. Assuming that the cluster finding and track fitting scales with the number of hits, one 

can predict a timing of 24 seconds for CBM on a processor with a performance of 1 kSI2k. Detailed 

simulations of the performance of the tracking system of the CMS and the ALICE experiments have 

been performed. The CMS tracker is very similar to the CBM, i.e. several layers of pixel and strip 

detectors. The DAQ-TRD [204] reports a tracking performance of 0.35 seconds for about 10 tracks in a 

b-jet (8 layers, on a 1 GHz CPU corresponding 1 kSI2k). The seed finding takes less than 10%. Extrapolating 

the performance to 600 tracks would give a timing of 210 seconds, but it is not clear how much 

of the processing time is due to pile-up. The ALICE barrel tracker consists of the TPC as the pattern 

recognition device and 6 layers of silicon detectors, the ITS (2 pixel, 2 drift and 2 strip detectors). The 

trackers for the High Level Trigger in ALICE have been optimized for speed without deteriorating the 

tracking efficiency and resolution. Both trackers, a cluster finder / track follower approach and a Hough 

transformation tracker, need about 3 seconds on a 1 kSI2k machine. Taking these tracks as seeds for a 

Kalman filter into the ITS, one needs one additional second for the global track fit. 

Today’s machines typically have a compute power of 2 kSI2k. If one would try to analyse level-1 selected 

CBM-events (about 1/2 of the multiplicty of a central collision) today, one would need about one second

10.7. CBM Simulation and Analysis Framework 275 

(ALICE HLT) or 6 seconds (NA49) per event. Processing at 25 kHz would require 2500 (150000 resp.) 

CPUs. Assuming an increase of the computational performance at equal cost of either 30% per year or a 

factor 2 in 18 months, the number of CPUs would decrease in 2015 by a factor of 14 - 100, resulting in 

250 - 900 CPUs in the case of a fast optimized tracker. Clusters of such a size and even larger ones are 

state-of-the-art today. The cost of a 900 CPU cluster is about 2 M. 

10.6.5 Offline computing 

Taking into account, (a) that the experiment will take data over several months a year, (b) that the online 

event selection schemes are very demanding and require a good alignment of the detectors and an online 

calibration strategy and (c) the costs of the archiving and retrieving system, the online reconstruction 

should be sufficiently efficient and accurate so that a second reconstruction and analysis pass, called 

offline processing, is not necessary. Only a limited re-processing e.g. for selected events or selected 

sub-detectors, may be considered. 

10.6.6 Cost estimate 

The costs for the HLPS cluster will be about 2 M. The yearly costs for mass storage based on fast disks, 

including for servers, comes in addition. Finally, the physics analysis of the ESDs requires computing 

power. ALICE estimates that the CPU power needed for reconstruction (Tier0/Tier1) is approximately 

the same as for analysis (Tier1/Tier2). At CBM, most of the reconstruction and some fraction of the 

physics analysis are already done by the online system, so 1-1.5 M for central plus 2-3 M for decentral 

offline computing seem to be sufficient. 

10.7 CBM Simulation and Analysis Framework 

10.7.1 Introduction 

The development of the current simulation and analysis framework has started at the end of 2003. The 

decision was taken at the time to build the simulation tool for the Technical Design Reports of the CBM 

detector using the OO programming technique and C++ as an implementation language. 

10.7.2 Design and implementation 

The schematic design of the CBM framework (CbmRoot) is shown in Fig. 10.15. 

The CBM framework design follows some basic requirements: 

• The framework is based on the ROOT system. 

• The framework should give the user the possibility to create simulated data and also to perform 

data analysis. 

• Geant3 and Geant4 transport engine should be supported. 

• The user code that creates simulated data should not depend on a particular monte carlo engine. 

For that purpose, the virtual monte carlo interface from the ROOT system has been used. 

It should also provide some basic functionalities:


Magnet 

Target 

PIPE 

Cave 

STS 

TRD 

TOF 

RICH 

ECAL 

Geometry 

Manager 

GeoInterface 

Module 

Detector 

Field Map 

ROOT 

Magnetic 

Field 

Virtual MC 

Run Manager 

Tasks 

Delta 

Primary 

Generator 

digitizers 

Tracking 

Geant3 

Geant4 

FLUKA 

IO Manager 

RunTime 

DataBase 

EVGEN 

Root files 

MCPoints, Hits, 

Digits, Tracks 

Oracle 

Configuration 

Parameters 

Geometry 

Root files 

Configuration 

Parameters 

Geometry 

Urqmd 

Pluto 

ASCII 

Particle 

Generator 

Figure 10.15: Schematic design of the CBM simulation and analysis framework 

• Input/Output procedures. 

• Parameters definition ( geometry of the detectors, physic process definition, hit digitization, etc.) 

• Implementation of the algorithms: the analysis should be organized in tasks. 

The CBM Framework incorporates these requirements by defining the following set of classes: 

10.7.2.1 The Run Manager class: CbmRun 

This class controls the whole simulation and analysis program, providing methods to configure and 

add the different tasks which can be realized. For the simulation part, this includes methods to set the 

different: 

• input and output files 

• event generators 

• monte carlo transport engines 

• magnetic field map definitions 

• material and medium definitions 

• passive and active detector definitions 

In the analysis case, the same class is used to organize the reconstruction process into a list of separated 

tasks, giving the user the possibility to switch on/off part of the reconstruction chain at run time.

10.7. CBM Simulation and Analysis Framework 277 

10.7.2.2 The Monte Carlo Application: CbmMCApplication 

This class uses the services of the ROOT Virtual Monte Carlo Application interface ( class TVirtualM- 

CApplication ) to define the actions at each stage of the simulation run. These are: 

• Geometry construction 

• Geometry initialization 

• Storage of primary track in an external stack container 

• Pre-tracking action 

• Stepping action and dispatching the hit processing to individual sensitive detectors 

• Post-tracking action 

10.7.2.3 The Input/Output Manager: CbmRootManager 

This class is responsible of writing and reading all persistent data objects created in memory during a 

simulation run. The storage of all information collected by the different sensitive detectors is done on 

an event by event basis (an event means in this context one interaction between one beam particle and 

the target). All persistent objects are serialized and stored into binary ROOT files. An interface class 

(CbmMCPoint) is provided to define the structure of registered hits in a detector. Each detector can 

then provide a more specific implementation following the CbmMCPoint API. All registered hits will 

be collected into dedicated lists, one list corresponding to one detector entity. The ROOT class TTree is 

used to organize the output data into a "ntuple like" data structure. In the analysis case, the CbmRoot- 

Manager provides methods to read this information. The user can select a subset of all registered list 

corresponding to his analysis tasks definition. A partial input/ouput mechanism is supported. 

10.7.2.4 Parameter classes 

Classes to contain and to manage all the numerical information needed to process the data (parameter). 

In order to analyse the simulated data, several numerical parameters are needed, as for example, calibration/digitization 

parameters or geometry positions of detectors. One common characteristic to most of 

these parameters is that they will go through several different versions corresponding, for example, to 

changes in the detectors definition or any other condition. This makes it necessary to have a parameter 

repository with a well-defined versioning system. The runtime database (realized through the CbmRuntimeDb 

class) is such a repository. Different inputs are supported : Ascii format, ROOT binary format 

and Oracle Database input. 

10.7.2.5 Tasks Classes (CbmTask) 

For each event we need to accomplish various tasks or reconstruction algorithms. The CbmTask is 

an abstract class defining a generic API allowing to execute one task and to navigate through a list of 

tasks. The user can create his own algorithm inheriting from CbmTask. Each task defines the relevant 

input data and parameter and creates its particular output data during the initialization phase. During the 

execution phase, the relevant input data and parameters are retrieved from the input file and the output 

data objects are stored in the output file.

278 Data Acquisition, Event Selection, Controls, On-line/Off-line Computing

11 Beam requirements and run time estimate 

11.1 Beam requirements 

The research programme to be performed with the CBM experiment requires the following beam performances: 

• Heavy-ion beam intensities of 10 9 particles per second up to the energies of 35 AGeV for U and 

45 AGeV for nuclei with Z = 0.5 A. 

• Proton beams with intensities of 10 10 protons per second up to an energy of 90 GeV 

• Beam spot at the target position not larger than 5 mm 2 . 

• The beam profile should be very narrow, the intensity outside a radius of 3 mm from the beam axis 

(halo) should be below 10 −6 of the total beam intensity in order to avoid damages of the Silicon 

Pixel detectors. 

• The beam intensity fluctuations should not exceed a factor of 2 with respect to the average value 

during extraction. 

11.2 Runtime estimate 

The envisaged experimental programme includes the following topics: 

• Compressed baryonic matter studies: systematic investigation of nucleus-nucleus (A+A) collisions 

at beam energies ranging from 2 - 45 AGeV (Z/A = 0.5) and up to 35 AGeV for Z/A = 0.4; 

• Measurements on the production of strangeness, low-mass vector mesons, open and hidden charm, 

and bottonium and in proton-nucleus collisions up to energies of 90 GeV. These data serve as 

reference for heavy-ion collisions (nuclear matter at saturation density). Moreover, data on charm 

and bottom production at threshold energies (which do not exist up to now) will shed light on the 

production mechanisms of heavy quarks. 

• Measurement of elementary production cross sections of charm (charmonium, D mesons), light 

vector mesons, multistrange hyperons, and exotica like pentaquarks etc. in proton-proton collisions 

at beam energies up to 90 GeV. 

The Data Acquisition (DAQ) System of CBM will be able to record minimum-bias heavy-ion collision 

events at a rate of 25 kHz. A high-speed trigger system will be used for the measurement of rare probes 

such as charmonium, and D mesons. 

279


11.2.1 Nucleus-nucleus collisions 

11.2.1.1 J/ψ meson measurements 

The J/ψ multiplicity is expected to be about 5 · 10 −6 per minimum bias Au+Au collision at 25 AGeV 

[205]. This value is assumed to be one quarter of the value for central collisions (see figure 11.1). 

Taking into account the branching ratio of 6% for the decay into a lepton pair and the efficiency (incl. 

acceptance and trigger) of 0.1 the detected J/ψ yield is about 3 · 10 −8 per event. At a reaction rate of 10 

MHz the number of recorded J/ψ mesons is about 2.6 · 10 4 per day. For the J/ψ measurement the trigger 

will require an (identified) e+e- pair with a transverse momentum above 1 GeV/c for each lepton. The 

primary reaction rate of 10 MHz will be reduced by the trigger conditions down to a level which can be 

handled by the DAQ system. 

beam energy J/ψ mult. J/ψ yield runtime 

(AGeV) min. bias detected per week (weeks) 

10 5 · 10 −8 1.8 · 10 3 10 

15 6 · 10 −7 2.2 · 10 4 10 

20 2 · 10 −6 7 · 10 4 10 

25 5 · 10 −6 1.8 · 10 5 5 

30 1.0 · 10 −5 3.6 · 10 5 2 

35 1.5 · 10 −5 5.4 · 10 5 1 

Figure 11.1: D mesons and J/ψ multiplicities 

calculated for central Au+Au 

collisions using the HSD transport code 

Table 11.1: Expected statistics and run time for J/ψ measurements in minimum bias Au+Au collisions 

The total run time for the J/ψ measurements in the Au+Au system amounts to about 1 year including 

commissioning (see table 11.1). Another year is planned for an intermediate mass system (A=100) and 

for a light collision system such as C+C up to 45 AGeV beam energy. The integral run time for J/ψ 

measurements in A+A collisions is about 2 years. 

11.2.1.2 D meson measurements 

According to figure 11.1 the D 0 meson multiplicity is about 1.2 · 10 −4 for central Au+Au collision at 

25 AGeV corresponding to 3 · 10 −5 for minimum bias collisions. Taking into account the branching

11.2. Runtime estimate 281 

ratio of about 4% for the decay into a kaon-pion pair, a geometrical acceptance of 0.5, and a trigger 

efficiency of 0.1 the detected D 0 yield is 6 · 10 −8 per event. According to first estimates (see chapter 18) 

it will be possible to develop a D 0 meson trigger based on the displaced vertex of the kaon and the pion. 

This requires high-resolution online tracking and particle identification. According to the simulations 

it should be possible to reduce the primary reaction rate of 10 MHz to about 10 kHz by the trigger 

(suppression factor of about 1000). Consequently, the recorded number of D 0 mesons in minimum bias 

Au+Au collisions at 25 AGeV is about 0.6 per second and about 5 · 10 4 per day. The D 0 measurements 

can be performed in parallel to the J/ψ experiments. In addition we will measure charged D mesons via 

their decay into a kaon and two pions. 

beam energy D 0 mult. D 0 yield runtime 

(AGeV) min. bias detected per week (weeks) 

15 7 · 10 −7 8 · 10 3 10 

20 7 · 10 −6 8 · 10 4 10 

25 3 · 10 −5 3.3 · 10 5 5 

30 7 · 10 −5 8 · 10 5 2 

35 1.3 · 10 −4 1.5 · 10 6 1 

Table 11.2: Expected statistics and run time for D 0 meson measurements in minimum bias Au+Au collisions 

11.2.1.3 Low-mass vector meson measurements 

The ρ-meson multiplicity is about 22 per central Au+Au collision at 25 AGeV corresponding to about 5 

per minimum bias collision. The branching ratio for the decay into an electron-positron pair is 4.5·10 −5 . 

Assuming a detection efficiency of 0.1 (incl. geometrical acceptance) the number of measured ρ-mesons 

is expected to be about 2.2 · 10 −5 per event. The combinatorial background in the electron-positron 

invariant mass spectrum has two major sources: Dalitz decays of the π0 (and η )-mesons and γ-conversion 

in the target and the detector materials. The gamma rays stem predominantly from π0 decays. According 

to simulations, about 40 Cherenkov rings are found in the RICH detector in a central Au+Au collision 

at 25 AGeV. Consequently, a trigger on double rings will not reduce considerably the primary reaction 

rate. Therefore, for the following beam time estimate we do not assume to use a trigger. 

Figure 11.2: Multiplicity of low-mass 

vector mesons calculated for central 

Au+Au collisions using the HSD transport 

code 

With a recorded minimum bias event rate of 25 kHz (about 2 · 10 9 events per day, corresponding roughly 

to the statistics in figure 4.4), we expect to record about 5 · 10 4 ρ-mesons per day for Au+Au collisions


at 25 AGeV. The production of low-mass vector mesons will be systematically studied in A+A collisions 

for various A in the beam energy range from 8 to 45 AGeV. Assuming a run time of 2 weeks for each 

A+A system with A = 200, 100, 12 and 6 energy steps the total run time is about 36 weeks beam on 

target. 

11.2.1.4 Hyperons, antiprotons, event-by-event fluctuations, collective flow phenomena, exotica 

No dedicated trigger is required for the measurement of (multi-strange) hyperons, of antiprotons, of 

fluctuations and of hadron flow. For example, the multiplicity of Ω − -hyperons in Au+Au collisions is 

about 4 · 10 −2 . Assuming a detection efficiency of 1 % and a minimum bias event rate of 25 kHz we can 

collect about 10 Ω − per second. 

Moreover, the CBM detector is well suited to search for exotic objects like multiquark systems, shortlived 

multistrange clusters, kaonic bound systems, etc. 

Billions of minimum bias events can be taken within one day. The data can be taken either in short 

dedicated runs or in parallel to other measurements with a down-scaled minimum bias trigger. 

11.2.1.5 Medium energy research programme with HADES 

The beam energy range between 2 and 8 AGeV can be studied with the HADES spectrometer. The 

research program includes the measurement low-mass-vector mesons (ρ,ω,φ ) and hadrons in nucleusnucleus, 

proton-nucleus and proton-proton collisions. The integral run time is expected to be about 2 

years. 

11.2.2 Proton-nucleus and proton-proton collisions 

11.2.2.1 J/ψ meson measurements 

One year of run time is needed for the J/ψ measurements in proton-nucleus collisions using three different 

nuclear targets and about 10 proton beam energies. Several months of run time is needed for the 

measurement of the J/ψ production excitation function in proton-proton collisions at beam energies close 

to the threshold. 

11.2.2.2 D meson measurements 

The D meson measurements in p+A and p+p collisions are particularly important as a reference for the 

A+A data. No data on D meson production are available at proton energies below 200 GeV. We plan to 

measure the excitation function of open charm production in proton induced reactions (targets H, C, Au) 

from threshold beam energies to 90 GeV. Like in A+A collisions, the D meson measurements will run in 

parallel to the charmonium experiments. 

11.2.2.3 Low-mass vector meson measurements 

The production of low-mass vector mesons will be systematically studied in p+A and p+p collisions for 

various A in the beam energy range from 8 up to 90 GeV for proton beams. As we have not anticipated 

a dedicated trigger for low-mass vector mesons, we will also take data on hyperons, antiprotons, and 

exotica during these measurements. For these proton induced reactions we expect a total run time of 

about 30 weeks.

11.2. Runtime estimate 283 

11.2.2.4 Exploratory study of Bottonium and B-meson production 

The production cross section of bottonium has not been measured in proton-induced reactions below 

proton energies of about 200 GeV. The ϒ meson can be identified via its dilepton decay which has a 

branching ratio of 2.4% for ϒ → e + e − and 2.5% for ϒ → µ + µ − . The production threshold for upsilon 

(mass 9.46 GeV/c 2 ) in p+p collisions (fixed target) is at a laboratory energy of about 65 GeV. Extrapolating 

from 200 GeV down to 90 GeV one estimates a cross section for upsilon production of about 

0.1 pb/nucleon [206] for proton-nucleus collisions (corresponding to about 0.02 nb for p+Au collisions). 

With a reaction cross section for p+Au of 1.5 b one obtains a ϒ multiplicity of about 10 −11 . Assuming 

a proton beam intensity of 1 · 10 10 /s and a 5% interaction target, the rate of produced ϒ mesons then is 

0.005/s. Taking into account the branching ratio and a detection efficiency of 5%, the rate of measured 

ϒ mesons is about 0.02 per hour or 13 ϒ per month. 

The production cross section for open beauty production in proton-nucleus collisions has been measured 

at 800 GeV [207]. B ± mesons decay into J/ψ and a K meson with a branching ratio of 10 −3 , and can 

be identified by a displaced vertex of a J/ψ meson. By extrapolating theoretical calculations down to 

90 GeV one estimates a cross section for B ± meson production of about 1 pb/nucleon [206] for protonnucleus 

collisions (corresponding to about 0.2 nb for p+Au collisions). With a reaction cross section for 

p+Au of 1.5 b one obtains a B ± meson multiplicity of about 10 −10 . Assuming a proton beam intensity of 

1·10 10 /s and a 5% interaction target, the rate of produced B ± mesons then is 0.05/s. Taking into account 

the branching ratio of 10 −3 and a detection efficiency of 5%, the rate of measured B ± mesons is about 

0.01 per hour corresponding to about 7 B ± per month. 

According to these estimates a decent measurement of open or hidden beauty at SIS 300 energies would 

require higher beam intensities than 10 10 protons per second. On the other hand, the production cross 

sections close to threshold are not known, and an exploratory measurement with a run time of about 1 

month should be performed.

284 Beam requirements and run time estimate

Part III 

Physics performance 

285

12 Experimental conditions 

12.1 Hit densities and rates 

The expected particle rates and hit densities have been calculated for inclusive and central Au+Au 

collisions at 25 AGeV. The events are generated by UrQMD and transported through the setup using 

GEANT4. The setup comprises the following detector subsystems: 7 Silicon Tracking Stations inside a 

dipole magnet with field on, the RICH detector, 3 TRD stations, the RPC stop wall, and the ECAL. The 

particle yields include both primary charged particles as generated by UrQMD, and secondary charged 

particles - mostly electrons and positrons - created in the detector materials. The primary and secondary 

particle yields are shown separately and summed up in figure 12.1. 

number of charged particle / event 

1200 

1000 

800 

600 

400 

200 

all 

primary 

secondary 

0 2 4 6 8 10 12 

z (m) 

Figure 12.1: Number of charged particles from central Au+Au collisions at 25 AGeV as registered by the detector 

stations along the beam axis according to a calculation using UrQMD and GEANT4. Red curve: primaries, blue 

curve: secondaries, black curve: sum of both. 

The two-dimensional hit-density distributions in the last STS station, in the 3 TRD stations, in the RPC, 

and in the ECAL are shown in figure 12.2. The charged particle yields are calculated for central Au+Au 

collisions at 25 AGeV, and include both primaries and secondaries. The yields in the ECAL include also 

neutral particles (gammas and neutrons). 

The one-dimensional hit density distributions for central Au+Au collisions at 25 AGeV are presented in 

figure 12.3 for the first 6 Silicon Tracking Stations, and in figure 12.4 for the last STS station, the 3 TRD 

stations, the RPC, and the ECAL. The distributions are calculated along the horizontal (red histograms) 

and the vertical symmetry axis (blue histograms) of the detectors. 

The expected rates calculated for inclusive Au+Au collisions at 25 AGeV (at a reaction rate of 10 MHz) 

are presented in figure 12.5 for the first 6 Silicon Tracking Stations, and in figure 12.6 for the last STS 

station, the 3 TRD stations, the RPC, and the ECAL. 

287


STS7, z = 1(m) 

y (cm) 

50 

40 

30 

20 

10 

0 

-10 

-20 

-30 

-40 

-50 

-50 -40 -30 -20 -10 0 10 20 30 40 50 

TRD2, z = 6(m) 

y (cm) 

400 

300 

200 

100 

0 

-100 

-200 

-300 

x (cm) 

-400 

0 

-400 -300 -200 -100 0 100 200 300 400 

TOF, z = 10(m) 

y (cm) 

600 

400 

200 

0 

-200 

-400 

x (cm) 

-600 

0 

-600 -400 -200 0 200 400 600 

x (cm) 

1 

0.8 

0.6 

0.4 

0.2 

0 

0.022 

0.02 

0.018 

0.016 

0.014 

0.012 

0.01 

0.008 

0.006 

0.004 

0.002 

0.01 

0.008 

0.006 

0.004 

0.002 

TRD1, z = 4(m) 

y (cm) 

y (cm) 

300 

200 

100 

0 

-100 

-200 

-300 

0 

-300 -200 -100 0 100 200 300 

TRD3, z = 8(m) 

500 

400 

300 

200 

100 

0 

-100 

-200 

-300 

-400 

x (cm) 

-500 

0 

-500-400-300-200-100 0 100 200 300 400 500 

ECAL, z = 12(m) 

x (cm) 

-800 

-800 -600 -400 -200 0 200 400 600 800 

Figure 12.2: Two-dimensional density distributions (hits/cm 2 ) of charged (primary and secondary) particles in 

the last STS station, in the 3 TRD stations, in the RPC and in the ECAL for central Au+Au events at 25 AGeV. 

The hits in the ECAL include both charged particles and neutrals (gammas and neutrons). 

y (cm) 

800 

600 

400 

200 

0 

-200 

-400 

-600 

x (cm) 

0.04 

0.035 

0.03 

0.025 

0.02 

0.015 

0.01 

0.005 

0.014 

0.012 

0.01 

0.008 

0.006 

0.004 

0.002 

0.014 

0.012 

0.01 

0.008 

0.006 

0.004 

0.002 

0

12.1. Hit densities and rates 289 

STS 1, z = 0.05(m) 

/event 

2 

Hits/cm 

2 

10 

10 

-2.5 -2 -1.5 -1 -0.5 0 0.5 1 1.5 2 2.5 

cm 

STS 3, z = 0.2(m) 

/event 

2 

Hits/cm 

10 

1 

-10 -8 -6 -4 -2 0 2 4 6 8 10 

cm 

STS 5, z = 0.6(m) 

/event 

2 

Hits/cm 

1 

-1 

10 

-30 -20 -10 0 10 20 30 

cm 

STS 2, z = 0.1(m) 

/event 

2 

Hits/cm 

/event 

2 

Hits/cm 

2 

10 

10 

1 

10 

-1 

10 

-5 -4 -3 -2 -1 0 1 2 3 4 5 

cm 

STS 4, z = 0.4(m) 

1 

-20 -15 -10 -5 0 5 10 15 20 

cm 

STS 6, z = 0.8(m) 

/event 

2 

Hits/cm 

1 

-1 

10 

-40 -30 -20 -10 0 10 20 30 40 

cm 

Figure 12.3: Hit density of charged (primary and secondary) particles from central Au+Au collisions at 25 AGeV 

as calculated for the first 6 Silicon Tracking Stations. The red lines show the density profile along the x axis, the 

blue curves along the y axis. The difference between the red and blue distributions is due to the magnetic dipole 

field.


STS7, z = 1(m) 

/event 

2 

Hits/cm 

1 

-1 

10 

-2 

10 

-50 -40 -30 -20 -10 0 10 20 30 40 50 

cm 

TRD2, z = 6(m) 

/event 

2 

Hits/cm 

10 

-2 

-3 

10 

-400 -300 -200 -100 0 100 200 300 400 

cm 

TOF, z = 10(m) 

/event 

2 

Hits/cm 

10 

-2 

-3 

10 

-600 -400 -200 0 200 400 600 

cm 

TRD1, z = 4(m) 

/event 

2 

Hits/cm 

/event 

2 

Hits/cm 

-2 

10 

-3 

10 

-300 -200 -100 0 100 200 300 

cm 

TRD3, z = 8(m) 

-2 

10 

-3 

10 

-500-400-300-200-100 0 100 200 300 400 500 

cm 

ECAL, z = 12(m) 

/event 

2 

Hits/cm 

10 

-2 

-3 

10 

-4 

10 

-800 -600 -400 -200 0 200 400 600 800 

cm 

Figure 12.4: Hit density of charged (primary and secondary) particles from central Au+Au collisions at 25 AGeV 

as calculated for the last STS station, the 3 TRD stations, the RPC and the ECAL. The yields in the ECAL include 

both charged particles and neutrals (gammas and neutrons). The red lines show the density profile along the x 

axis, the blue curves along the y axis. The difference between the red and blue distributions is due to the magnetic 

dipole field.


STS 1, z = 0.05(m) 

2 

MHz/cm 

2 

10 

10 

-2.5 -2 -1.5 -1 -0.5 0 0.5 1 1.5 2 2.5 

cm 

STS 3, z = 0.2(m) 

2 

MHz/cm 

10 

1 

-10 -8 -6 -4 -2 0 2 4 6 8 10 

cm 

STS 5, z = 0.6(m) 

2 

MHz/cm 

10 

1 

-1 

10 

-30 -20 -10 0 10 20 30 

cm 

STS 2, z = 0.1(m) 

2 

MHz/cm 

2 

MHz/cm 

2 

10 

10 

1 

-5 -4 -3 -2 -1 0 1 2 3 4 5 

cm 

STS 4, z = 0.4(m) 

10 

1 

-20 -15 -10 -5 0 5 10 15 20 

cm 

STS 6, z = 0.8(m) 

2 

MHz/cm 

1 

-1 

10 

-40 -30 -20 -10 0 10 20 30 40 

cm 

Figure 12.5: Rates of charged (primary and secondary) particles in the first 6 Silicon Tracking Stations for 

minimum bias Au+Au events at 25 AGeV assuming an interaction rate of 10 MHz. The red lines show the density 

profile along the x axis, the blue curves along the y axis. The difference between the red and blue distributions is 

due to the magnetic dipole field.


STS7, z = 1(m) 

2 

MHz/cm 

1 

-1 

10 

-50 -40 -30 -20 -10 0 10 20 30 40 50 

cm 

TRD2, z = 6(m) 

2 

MHz/cm 

-2 

10 

-3 

10 

-400 -300 -200 -100 0 100 200 300 400 

cm 

TOF, z = 10(m) 

2 

MHz/cm 

-2 

10 

-3 

10 

-600 -400 -200 0 200 400 600 

cm 

TRD1, z = 4(m) 

2 

MHz/cm 

2 

MHz/cm 

-1 

10 

-2 

10 

-300 -200 -100 0 100 200 300 

cm 

TRD3, z = 8(m) 

-2 

10 

-3 

10 

-500-400-300-200-100 0 100 200 300 400 500 

cm 

ECAL, z = 12(m) 

2 

MHz/cm 

-2 

10 

-3 

10 

-800 -600 -400 -200 0 200 400 600 800 

cm 

Figure 12.6: Rates of charged (primary and secondary) particles in the last STS station, in the 3 TRD stations, 

in the RPC and in the ECAL for minimum bias Au+Au events at 25 AGeV assuming an interaction rate of 10 

MHz. The rates in the ECAL include both charged particles and neutrals (gammas and neutrons). The red lines 

show the density profile along the x axis, the blue curves along the y axis. The difference between the red and blue 

distributions is due to the magnetic dipole field.


The following tables list hit rates, hit densities and detector areas for the TRD and the RPC for different 

emission angles. The hit densities refer to charged primary and secondary particles emitted in central 

Au+Au collisions at 25 AGeV as calculated with UrQMD. The rates are calculated for 10 MHz minimum 

bias Au+Au collisions at 25 AGeV. The cell size was determined by the requirement that the occupancy 

is below 5 %. Due to position resolution requirements the maximum cell sizes are limited to 10 cm 2 for 

the TRD and to 20 cm 2 for the RPC. 

The detector areas were calculated assuming an elliptic shape which takes into account the deflection of 

charged particles in horizontal direction, i.e. the horizontal dimension of the detectors is 1.5 times the 

vertical dimension. It should be noted that for the TRD with pad readout the quoted number of cells 

correspond to 10% occupancy due to the fact that in average two pads fire per particle. Each of the TRD 

stations will consist of 3-4 layers which means that the number of electronic channels is 3-4 times larger 

than the number of cells quoted in the tables. 

emission angle [mrad] TRD 1 dist. 4 m 

rates area N cell size # o f cells 

[kHz/cm 2 ] [m 2 ] [cm −2 ] [cm 2 ] 

50 ÷ 100 100. 0.52 4.5·10 −2 1.1 4700 

100 ÷ 150 53. 0.96 2.6·10 −2 1.9 5100 

150 ÷ 200 26. 1.38 1.4·10 −2 3.6 3800 

200 ÷ 250 17. 1.82 0.78·10 −2 6.4 2800 

250 ÷ 300 9.6 2.29 0.46·10 −2 10. (11.) 2300 

300 ÷ 350 7.1 2.83 0.34·10 −2 10. (15.) 2800 

350 ÷ 400 4.4 3.43 0.21·10 −2 10. (24.) 3400 

400 ÷ 450 2.0 4.12 0.09·10 −2 10. (54.) 4100 

450 ÷ 500 0.9 4.91 0.04·10 −2 10. (121.) 4900 

sum 22.3 33900 



[kHz/cm 2 ] [cm 2 ] [cm −2 ] [cm 2 ] 

50 ÷ 100 50. 1.17 2.2·10 −2 2.2 5300 

100 ÷ 150 25. 2.17 1.3·10 −2 3.9 5600 

150 ÷ 200 13. 3.09 0.66·10 −2 7.5 4100 

200 ÷ 250 7.5 4.09 0.36·10 −2 10. (14.) 4100 

250 ÷ 300 5.0 5.17 0.24·10 −2 10. (21.) 5200 

300 ÷ 350 3.3 6.37 0.17·10 −2 10. (30.) 6400 

350 ÷ 400 2.1 7.72 0.10·10 −2 10. (48.) 7700 

400 ÷ 450 1.0 9.26 0.05·10 −2 10. (106.) 9300 

450 ÷ 500 0.4 11.0 0.02·10 −2 10. (240.) 11000 

sum 50.1 58700




[kHz/cm 2 ] [cm 2 ] [cm −2 ] [cm 2 ] 

50 ÷ 100 32. 2.08 1.4·10 −2 3.6 5800 

100 ÷ 150 15. 3.85 0.7·10 −2 6.7 5700 

150 ÷ 200 7.9 5.50 3.9·10 −3 10. (13.) 5500 

200 ÷ 250 4.8 7.27 2.3·10 −3 10. (22.) 7300 

250 ÷ 300 2.7 9.19 1.4·10 −3 10. (36.) 9200 

300 ÷ 350 2.0 11.3 0.95·10 −3 10. (52.) 11300 

350 ÷ 400 1.3 13.7 0.65·10 −3 10. (77.) 13700 

400 ÷ 450 0.6 16.5 0.29·10 −3 10. (170.) 16500 

450 ÷ 500 0.3 19.6 0.13·10 −3 10. (383.) 19600 

sum 89.1 94600 

emission angle [mrad] TOF dist. 10 m 


[kHz/cm 2 ] [cm 2 ] [cm −2 ] [cm 2 ] 

50 ÷ 100 20. 3.19 8.9·10 −3 5.6 5700 

100 ÷ 150 13. 5.83 6.5·10 −3 7.7 7600 

150 ÷ 200 6.6 8.08 3.2·10 −3 15.7 5100 

200 ÷ 250 4.5 10.2 2.0·10 −3 20. (25.) 5100 

250 ÷ 300 2.6 12.3 1.4·10 −3 20. (37.) 6200 

300 ÷ 350 2.1 14.3 1.0·10 −3 20. (50.) 7100 

350 ÷ 400 1.8 16.1 0.69·10 −3 20. (73.) 8000 

400 ÷ 450 0.8 17.7 0.31·10 −3 20. (162.) 8800 

450 ÷ 500 0.4 19.2 0.14·10 −3 20. (365.) 9600 

sum 106.8 63200

12.2. Hadron multiplicities and momentum distributions from UrQMD calculations 295 

12.2 Hadron multiplicities and momentum distributions from UrQMD 

calculations 

Table 12.1 lists the particle multiplicities from central Au+Au collisions as calculated with the UrQMD 

event generator. The laboratory momenta of pions, protons, and kaons calculated with UrQMD for 

central Au+Au collisions are shown in the figures 12.7 for beam energies of 15, 25, and 35 AGeV. 

particle multiplicity per event 

15 25 35 

AGeV AGeV AGeV 

γ 10 14 17 

π + 240 332 398 

π − 268 362 429 

π 0 266 365 437 

η 25 36 45 

K + 30 41 49 

K − 7 13 18 

K 0 31 42 50 

p 165 161 159 

n 183 176 173 

Λ 23 28 31 

Σ + 7 9 10 

Σ 0 7 9 9 

Σ − 8 10 10 

Ξ 0 0.617 0.961 1.191 

Ξ − 0.637 0.983 1.210 

Ω − 0.011 0.022 0.033 

¯p 0.024 0.139 0.328 

¯n 0.022 0.137 0.323 

¯Λ 0.017 0.103 0.244 

Σ ¯+ 

Σ¯ 0.005 0.031 0.072 

0 Ξ¯ 0.005 0.029 0.068 

0 0.002 0.010 0.028 

Ξ¯− 0.002 0.011 0.027 

Table 12.1: Particle multiplicities in UrQMD, central Au+Au collisions, impact parameter b=0 fm.


yield/event 

yield/event 

yield/event 

10 

1 

-1 

10 

-2 

10 

-3 

10 

-4 

10 

10 

1 

-1 

10 

-2 

10 

-3 

10 

10 

1 

-1 

10 

-2 

10 

-3 

10 

Au+Au, 15 AGeV/c 

π+ 

0 5 10 15 20 25 30 

p lab, GeV/c 

0 5 10 15 20 25 30 

p lab, GeV/c 

π- 

p 

K+ 

K- 

Au+Au, 25AGeV/c 

π+ 

0 5 10 15 20 25 30 

p lab, GeV/c 

π- 

p 

K+ 

K- 

Au+Au, 35AGeV/c 

π+ 

Figure 12.7: Particle yields per event as function of laboratory momentum calculated with the UrQMD model 

for central Au+Au collisions at 15, 25, and 35 AGeV. 

π- 

p 

K+ 

K-

12.3. Hadron multiplicities from HSD calculations 297 

12.3 Hadron multiplicities from HSD calculations 

As input for the simulations on rare probes like low-mass vector mesons, open and hidden charm we use 

multiplicity predictions calculated with the Hadron-String Dynamics (HSD) model version V2.4. The 

Hadron-String Dynamics (HSD) is a covariant microscopic transport model developed to simulate pionnucleus, 

proton-nucleus reactions and relativistic heavy-ion collisions. The HSD transport approach 

provides the numerical test-particle solution of a coupled set of relativistic transport equations for particles 

with in-medium self-energies. It is based on quark, diquark, string and hadronic degrees of freedom. 

High energy inelastic hadron-hadron collisions are in HSD described by FRITIOF 7.02 string model including 

PYTHIA and JETSET whereas low energy hadron-hadron collisions are modelled on experimental 

cross section. The transport approach is matched to reproduce the nucleon-nucleon, meson-nucleon 

and meson-meson cross section data in a wide kinematic range. Inside HSD it has been also taken into 

account the formation and multiple rescattering of leading pre-hadrons and hadrons. 

Table 12.2 lists the particle multiplicities calculated for central Au+Au collisions. The yield of charged 

D mesons is underestimated particularly for beam energies close to the threshold because the process of 

associated charm production via NN→DΛcN is not included in the HSD model calculation. 

particles mass 10 15 20 25 30 35 

(MeV) (AGeV) (AGeV) (AGeV) (AGeV) (AGeV) (AGeV) 

π 0 135 221 264 300 337 361 382 

π − 140 233 293 333 368 397 423 

π + 140 201 261 298 332 361 386 

η 550 16 23 29 33 37 40 

K 0 498 21 31 37 42 47 52 

¯K 0 498 3 7 9 12 14 17 

K + 494 20 28 35 40 45 48 

K − 494 3 7 9 13 16 18 

ρ 0 775 9 15 19 23 25 26 

ω 0 783 19 27 34 38 42 46 

φ 1020 0.12 0.50 0.83 1.28 1.24 1.50 

Λ 1115 20 26 30 32 34 35 

Σ 0 1193 5.99 8.09 9.20 10.06 10.56 10.76 

Σ + 1189 4.19 5.69 6.55 7.18 7.72 8.74 

Σ − 1197 3.30 4.04 4.61 5.46 5.37 5.67 

Ξ 0 1315 0.13 0.20 0.34 0.40 0.45 0.45 

Ξ − 1321 0.18 0.22 0.32 0.48 0.33 0.42 

D 0 1864 1.15 · 10 −11 2.72 · 10 −7 6.67 · 10 −6 3.74 · 10 −5 1.11 · 10 −4 2.49 · 10 −4 

¯D 0 1864 8.76 · 10 −9 2.97 · 10 −6 3.05 · 10 −6 1.15 · 10 −4 2.78 · 10 −4 5.43 · 10 −4 

D + 1869 1.67 · 10 −11 3.34 · 10 −7 7.71 · 10 −6 4.17 · 10 −5 1.21 · 10 −4 2.67 · 10 −4 

D − 1869 6.76 · 10 −9 2.28 · 10 −6 2.34 · 10 −5 8.91 · 10 −5 2.16 · 10 −4 4.23 · 10 −4 

D + s 1969 2.13 · 10 −14 1.71 · 10 −8 7.50 · 10 −7 5.43 · 10 −6 1.84 · 10 −5 4.53 · 10 −5 

D − s 1969 2.18 · 10 −11 6.67 · 10 −8 1.26 · 10 −6 6.59 · 10 −6 1.92 · 10 −5 4.29 · 10 −5 

J/Ψ 3097 1.74 · 10 −7 2.44 · 10 −6 8.37 · 10 −6 1.92 · 10 −5 3.45 · 10 −5 5.49 · 10 −5 

Ψ ′ 

3686 1.07 · 10 −10 1.69 · 10 −8 9.09 · 10 −8 2.56 · 10 −7 5.66 · 10 −7 9.96 · 10 −7 

Table 12.2: Particle multiplicities calculated for central Au+Au collisions (impact parameter of b = 0.5 fm) at 

different beam energies using the HSD code


According to the HSD model the multiplicity of the low-mass vector mesons (ρ 0 , ω 0 , and φ) varies with 

time during the collision. This is illustrated in figure 12.8 which shows the time dependent ρ (all charges), 

ω and φ meson multiplicities for central Au+Au collisions (impact parameter b=0.5fm) at a beam energy 

of 25 AGeV. We take the maximum value of the time dependent yield as input for the simulations (i.e. 

one third of the value for ρ 0 due to isospin). These yields are plotted in figure 12.9 as function of beam 

energy. 

Figure 12.8: Yield of ρ(all charges), ω, φ mesons as function of collision time, calculated with HSD for Au+Au 

collisions at 25 AGeV, impact parameter b = 0.5 fm 

Figure 12.9: ρ 0 , ω, and φ multiplicities calculated with HSD for central Au+Au collisions (impact parameter b 

= 0.5 fm) as function of beam energy.

12.4. Hadron multiplicities: experimental results 299 

12.4 Hadron multiplicities: experimental results 

Hadron multiplicities have been measured at AGS and SPS in Au+Au and Pb+Pb collisions by the E802 

and the NA49 collaborations [208, 209, 210, 211, 212, 213]. The results are listed in table 12.3. The 

measured pion yields are substantially lower than predicted by the transport models UrQMD and HSD, 

whereas the measured K + yields are 10-20 % higher than the results of the model calculations. 

10.6 20 30 40 

(AGeV) (AGeV) (AGeV) (AGeV) 

Apart 363±10 349±5 349±5 349±5 

π − 217.5±15 275±20 322±16 

π + 134±10 184.5±13 239±17 293±15 

K + 24±3 40±2 55.3±3.0 59.1±3 

K − 3.8±0.5 10.4±0.5 16.1±0.8 19.2±1.0 

φ 1.91±0.45 2.57±0.10 

Λ 18.1±1.9 45.6±3.4 

¯Λ 0.017±0.005 0.74±0.06 

Table 12.3: Hadron multiplicities measured in Au+Au and Pb+Pb collisions [208, 209, 210, 211, 212, 213] 

12.5 Knock-On electrons 

The flux of knock-on electrons produced by the very intense heavy-ion beam in the CBM target might 

deteriorate the performance of the Silicon Tracking Stations which are located in close vicinity of target. 

Simulations have been performed using the standard CBMRoot (version Oct 2004) and the Geant3 

code. The yield of knock-on electrons seen by the Silicon Tracking stations has been calculated for the 

following conditions: 

• a gold target of 0.25 mm thickness (1% interaction probability for a gold projectile) 

• gold beam at an energy of 25 AGeV 

• Carbon vacuum pipe of 0.5 mm thickness 

• distance between the target and the Silicon stations of 5, 10, 20, 40, 60, 80, and 100 cm 

• two magnetic field configurations: a symmetric field distribution with a maximum field value of 1 

T (version 3), and an asymmetric field distribution with a maximum field value of 1.4 T (version 

4). 

The distribution of the knock-on electrons at the STS planes is very inhomogeneous due to the focusing 

properties of the magnetic field. About 20% of the electrons are focused on two hot spots, as it illustrated 

in figure 12.10 for the first STS stations. In this case, the electron distribution peaks at x=7 and y=± 5 

mm, exhibiting densities of up to 2.4 electrons per cm 2 and per projectile. The total number of knock-on 

electrons hitting the STS per gold projectile is 12, 13., 8., 1., 0.4, 0.3, 0.1 (+-4%) for the first up to 

the seventh STS plane, respectively. At the maximum gold beam intensity, the electron flux in the hot 

spot area of the first STS plane amounts up to 2.4×10 9 /(cm 2 s). This effect will deteriorate the detection 

efficiency, in particular for negative particles, and will cause an enhanced radiation damage of the silicon 

detectors in the hot spot zone.


The calculation was performed with the asymmetric field configuration (1.4 T maximum field). For the 

symmetric field configuration with a maximum field of 1 T, the yield of knock-on electrons hitting the 

STS planes is higher (16 electrons per projectile in the first STS plane). This effect is due to the lower 

magnetic field. Due to the large off-axis value of the Bx and Bz components of the symmetric magnetic 

field the intensity of knock electrons in the sixth and seventh STS plane is even 10 times higher than in 

the case of 1.4 T field. 

The flux of knock-on electrons can be reduced by introducing an asymmetric lead absorber of 4 mm 

thickness close to the beam pipe. The conical absorber reduces the number of knock-on electrons hitting 

the tracking stations by about a factor of 2. The resulting electron yields in the different STS planes 

are shown in figure 12.11. A further reduction of the electron flux onto the STS planes by an additional 

magnetic field in the target area will be investigated. 

Y position [cm] 

2 

1 

0 

-1 

-2 

-2 -1 0 1 2 

X position [cm] 

Figure 12.10: Density distribution of knock-on electrons in the first STS plane 

Number of KO electrons per STS 

5 

4 

3 

2 

1 

0 

0 20 40 60 80 100 

STS Position [cm] 

Figure 12.11: Yield of knock-on electron per gold projectile in the 7 STS planes. 

2.4 

2.2 

2 

1.8 

1.6 

1.4 

1.2 

1 

0.8 

0.6 

0.4 

0.2 

0

13 Event reconstruction 

The event reconstruction in the CBM experiment is a challenging task due to the high charged particle 

multiplicities encountered in fixed target heavy ion collisions at the envisaged collision energies (up to 

700 tracks in the detector acceptance for central Au+Au collisions at 25 AGeV). It comprises local track 

finding and fitting in the STS and TRD, ring finding in RICH, cluster reconstruction in ECAL, global 

matching between STS, RICH, TRD, TOF and ECAL, and the reconstruction of primary and secondary 

vertices. In this section, we present the status of algorithms under development for fulfilling these tasks. 

Track reconstruction is most difficult in the STS because of the extreme hit densities of up to 100/cm 2 

in the first tracking station. Moreover, the STS is located in an inhomogeneous magnetic field causing 

a continuous variation of the curvature along the trajectories. The difficulties of track reconstruction in 

the STS are illustrated in figure 13.1 depicting a central Au+Au collision at 25 AGeV as calculated with 

UrQMD and GEANT. The track reconstruction routines have to fulfill the following requirements: 

• track reconstruction efficiency of better than 90 % also for secondary particles with low momenta, 

• momentum resolution better that δp/p = 1% for p > 1 GeV/c, 

• secondary vertex reconstruction with high efficiency and high resolution, 

• fast algorithms for high-resolution online triggering of displaced vertices of D mesons. 

The track reconstruction problem can be split into track finding and track fitting. Up to now, most efforts 

went into the problem of tracking in the STS as the most challenging task. Different approaches to 

both track finding and the reconstruction of the initial track parameters are under investigation. For the 

track finding, Hough transform, Cellular Automaton and track following methods have been used. The 

Kalman filter and global fitting methods like the polynomial approximation are applied to the problem 

of momentum reconstruction. The Kalman filter was also used for the determination of the primary and 

secondary vertices. 

Figure 13.1: Visualisation of a simulated central Au+Au event at 25 AGeV/c beam momentum in the STS region 

301


In a similar way to the tracking in the STS, the reconstruction of rings in the RICH can be separated into 

ring finding and fitting. For ring finding, the method of centre guidance was used as well as standalone 

ring finders using the Hough Transform or the Elastic Net algorithm. 

13.1 Track finding 

13.1.1 Hough Transform 

The Hough Transform is a global tracking algorithm and therefore its number of operations is proportional 

to the number of hits to process. It has the advantage that it is robust against noisy or missing 

detector hits and also sensitiv to ill-defined objects. We have implemented an algorithm based on the 

Hough Transform for fast tracking in the STS system in order to investigate its hardware (e. g. FPGA) 

applicability for a first trigger level. 

The Hough transform converts the coordinates of the detector hits of particle tracks into the space of 

track parameters. In the bending (x-z) plane projection, the trajectory of tracks starting from the origin 

(target) in z direction can be approximated as parabolas: 

x = 

n e By 

z 

2 pz 

2 

(13.1) 

where n is the number of elementary charges, pz the z component of the momentum and By the y component 

of the magnetic field. In case of an inhomogenous magnetic field By may be any function of 

the hit coordinates x and z. For tracks starting with an angle θ = arctan px/pz, x → zsinθ − xcosθ and 

z → zcosθ + xsinθ and thus 

1 

pz 

= 2 (zsinθ − xcosθ) 

ne By (zcosθ + xsinθ) 2 

(13.2) 

One track with the parameters pz and θ is represented as one point in the Hough space (see fig. 13.2 a). 

One measured point (x, z) can contribute to several tracks, but all these tracks obey the above equation. 

This leads to an appropriate curve in the Hough space (see fig. 13.2 b). Several points of one track lead 

to several curves in the Hough space, that all intersect at the point that describes the parameters of the 

track (see fig. 13.2 c). 

In the y-z-plane the tracks can be described approximately as a straight line from the origin. The mapping 

for this case is very simple: the parameter γ = arctan py/pz can be directly calculated from y/z. Due to 

multiple scattering of the particles during their path through the detector planes, the real shape of the 

tracks differs from an exact straight line. Therefore, also a slightly curved line has to be taken into 

account. 

The two projections lead to a 3-dimensional Hough transform according to the parameters 1/pz , px/pz 

and py/pz. Assuming a minimum momentum pz equivalent to a maximum bending, the Hough parameter 

space can be confined. After its discretisation, the transformed hits are accumulated in a histogram. Peaks 

in this histogram define the parameters of a found track in the detector. Due to the geometrical acceptance 

of the detector and according to the bending of the track, momentum cuts are applied. 

The Hough transform method was applied to simulated central Au+Au data, using the standard setup of 

the STS system. The space points of tracks in the five detector layers foreseen to be built of silicon strips 

(STS3 to STS7) have been discretised in 50 µm bins to match the detector spatial resolution. Figure 13.3 

illustrates the track finding procedure in the x-z projection.

13.1. Track finding 303 

a) 

b) 

c) 

x 

x 

x 

detector Hough space 

z 

1/Pz 

one track one point 


z 

1/Pz 

one hit one curve 


z 

1/Pz 

five hits five curves 

Figure 13.2: Hough transform of a track in the x-z-plane according to 1/pz and θ. a) One track in the detector 

is represented by one point in the Hough space. b) One hit in the detector can contribute to several tracks, that 

are represented by a curve in the Hough space. For this application, the real curves in the Hough space are close 

to straight lines. c) Each hit in each layer is transformed to its appropriate curve in the Hough space. All curves 

intersect at the point that describes the parameters of the track. 

The tracking efficiency of the algorithm is defined as the ratio of found tracks divided by all tracks with 

hits in at least three detector layers and a momentum larger than 1 GeV/c. Its momentum dependence is 

shown in figure 13.4 for 15, 25 and 35 AGeV/c beam momentum. The resolution of the Hough histogram 

is 127 × 383 × 191. The efficiency for tracks with a momentum less than 1 GeV/c is very low because 

of the confinement of the Hough space. However, for triggering on open charm and charmonium, low 

momentum tracks are not of interest. For momenta above 1 GeV/c, the efficiency is about constant, 

slightly rising towards higher momenta. 

Ghost tracks correspond to found peaks that are accumulated from hits of different MC tracks. The ghost 

θ 

θ 

θ


Figure 13.3: One 2-dimensional Hough plane filled with transformed hits. A central plane processing the hits 

near the beam pipe is shown here. Planes more apart from the beam pipe contain less transformed hits. There are 

seven peaks in the histogram (black points) corresponding to seven found particle tracks. A peak is defined by 

more than three hits in consecutive detector layers. Six peaks can be assigned to certain MC tracks. The lower 

most peak corresponds to no real track, but accumulates a peak from five hits of different tracks. 

Figure 13.4: Track finding efficiency for tracks with hits in at least three tracking stations as function of momentum 

using the Hough transform method for central Au+Au events at different beam momenta 

rate is highest for low and high momenta. At low momenta, the track multiplicity is highest and thus 

combinatorial coincidences are more likely. Tracks at high momenta are less numerous, but correspond 

to almost straight lines near the beam pipe with high track densities. The mean numbers of efficiency 

and ghost rate for the three beam momenta are given in table 13.1. While the efficiency is almost the 

same, the ghost rate increases quickly with beam momentum. 

Both the efficiency and the ghost rate depend on the multiplicity of the event, the number of detector


35 GeV 25 GeV 15 GeV 

number of STS hits/event 5000 4000 3500 

efficiency (P > 1 GeV/c) 91 % 90 % 91 % 

ghost rate 36 % 16 % 7 % 

Table 13.1: Efficiency and ghost rate of tracking using the Hough transform for central Au+Au events at different 

beam energies 

layers and the resolution in the Hough space. Better results can be expected for events with less tracks and 

detector setups with more detector layers. The efficiency can be improved by increasing the resolution 

of the histogram of the Hough transform. 

In section 10.4.2.1 a possible hardware implementation for the processing of a 10 Gbit/s input data stream 

of detector hit data in real time is described. The time needed for processing a central event is of the 

order of 100µs. The algorithm, in particular the adjustment to strip detectors, will be developed further 

when a more final state of the detector description is available. An application of the Hough transform 

for the TRD detector is in preparation. 

13.1.2 Cellular Automaton method 

Cellular automata have already successfully been used for data filtering and track searching in high 

energy physics [214, 215, 216, 217]. The use of a cellular automaton for track searching is motivated 

by its intrinsic simplicity resulting in considerably speeding up this part of the reconstruction process, 

which is usually the most time-consuming one. 

Cellular automata [218] are dynamical systems that evolve in discrete, usually two-dimensional spaces 

consisting of cells. Each cell can take several states. In the simplest case, the cell state can be described 

by a single-bit: 0 or 1. The laws of evolution are local, i.e., the dynamics of the system is determined 

by an unchanged set of rules (for example a table) that relates the new state of a cell to the states of its 

nearest neighbors. The update of the states of the cells is done simultaneously at discrete time instants. 

A cellular automaton possesses a number of advantageous features when used for track recognition. 

Being essentially local and parallel, cellular automata avoid exhaustive combinatorial searches, even 

when implemented on conventional computers. Since cellular automata operate with highly structured 

information (for instance sets of track segments connecting space-points), the amount of data to be 

processed in the course of the track search is significantly reduced. Further reduction of information to 

be processed is achieved by smart definition of the segment neighborhood. Usually cellular automata 

employ a very simple track model which leads to utmost computational simplicity and a fast algorithm. 

In the literature, two kinds of cellular automata for track searching are described: algorithms which are 

similar to the game “Life” and use space-points as cell units [214]; segment-based algorithms which use 

short track segments [215, 216, 217] as cell units. Here a segment-based cellular automaton is used. A 

simple 2D illustration of the cellular automaton algorithm is shown in Fig. 13.5. 

The cellular automaton is constructed by defining cells, neighbours, rules of evolution and time evolution. 

A cell is defined as a short track segment connecting two space-points on neighbouring stations. In order 

to make the algorithm faster, only integer cell states with a simple meaning are considered — the cell 

state defines the place of the segment in an optimising (in the sense of χ 2 ) sequence. In the beginning all 

cell states are set to unity because each segment can, in principle, initiate the optimal sequence. 

Two segments are considered neighbours if and only if they share a common space-point, and they form a 

curve which points to the target region. A parabolic representation of a track model in the bending plane


1 

2 

3 4 

Collect tracks Create tracklets 

Figure 13.5: A simple illustration of the cellular automaton algorithm. It creates tracklets, links and numbers 

them as possibly situated on the same trajectory, and collects tracklets into track candidates. 

is chosen as the simplest and therefore fastest approximation of a particle trajectory in an inhomogeneous 

magnetic field. 

The evolution process is divided into: 

• forward evolution when the automaton iteratively updates the states of all cells having neighbours; 

• backward pass when the automaton collects optimal sequences starting from cells with the highest 

states. 

During the forward evolution the automaton takes each cell and looks for its leftward neighbours. If there 

is such a neighbour and its state is equal to the cell’s state, the cell’s state will be increased by one. When 

the automaton completes a loop over all the cells, their previous states are simultaneously replaced by the 

increased ones. This process is iteratively repeated until there are no neighbouring cells with the same 

states. This procedure is a simplified version of the rule SAFE-PASS [218]. The forward evolution of 

the automaton is illustrated in Fig. 13.5. At the end of the forward evolution, the state of each cell is 

equal to the length of an optimising sequence that can be traced leftwards starting from this cell. 

The backward pass starts with investigating the set of cells that have the highest state. These cells are 

considered as the first segments of track candidates. For each cell the automaton performs a loop over the 

cell’s leftward neighbours looking for the best prolongation of the track candidate. To be a prolongating 

one a cell must have a state lower by unity. If such a cell is found, the automaton assigns it to the track 

candidate, looks for its leftward neighbours and so on. The candidate tracing stops when a segment with 

the state of unity is assigned to the candidate. The baseline algorithm marks all segments assigned to a 

track as used and the automaton proceeds by assembling the next track candidate starting with another 

cell that has the highest state and is not marked as already used. 

After the completion of the backward pass, an additional test of track qualities is performed. This provides 

the rejection of ghost tracks which could accidentally be reconstructed from hits belonging to 

different real tracks. A quality estimator which favours long and smooth tracks is used for each track. 

The algorithm was applied to central Au+Au collisions at 25 AGeV beam energy simulated by UrQMD 

and transported through the CBM detector setup. A position resolution of 10 µm in the STS was assumed. 

For the TRD, a strip readout of 5 mm long strips with 500 µm resolution was taken. Having 

the inhomogeneous magnetic field in STS, we use a straight line track model in the non-bending projec- 

5 

6


tion and a local parabolic approximation in the bending projection. In the field free region of the TRD 

detector the pure straight line approach is used in the algorithm. 

For evaluation purposes all simulated and reconstructed tracks are subdivided into several categories. 

The “all set” comprises tracks which intersect the sensitive regions of at least three tracking stations. A 

“reference” track should in addition have a momentum greater than 1 GeV/c. The reference set of tracks 

can also include tracks of particular physics interest such as secondary tracks from interesting decays 

and primary tracks coming from the target region. In addition to these tracks, the “extra set” of tracks, 

containing low-momentum tracks, is also considered. 

Efficiency 

1 

0.8 

0.6 

0.4 

0.2 

0 

0 2 4 6 8 10 12 14 16 18 20 

Momentum (GeV/c) 

Figure 13.6: Reconstruction efficiency versus momentum of particle in the STS sub-detector for the “all set” 

selection of tracks (see text) 

A reconstructed track is assigned to a generated particle if at least 70% of its hits have been caused by 

this particle. A generated particle is regarded as found if it has been assigned to at least one reconstructed 

track. If the particle is found more than once, all additionally reconstructed tracks are regarded as clones. 

A reconstructed track is called ghost if it is not assigned to any generated particle (70% criterion). 

Table 13.2 shows efficiencies of track finding in the STS detector for all sets of tracks obtained by 

applying the algorithm to central Au+Au events at 25 AGeV transported through the CBM detetcor setup. 

The reconstruction efficiency for reference primary tracks is 97%, while the efficiency of all reference 

tracks is slightly lower because of the presence of secondary tracks originating far downstream from the 

target region. The total efficiency (see figure 13.6) for all tracks is about 70% because of a large fraction 

of soft secondary tracks that suffers significant multiple scattering in the detector material. The track 

model must be also improved in order to increase the efficiency of slow tracks as they are more sensitive 

to the magnetic field. The clone rate is negligible; the ghost level is at 5%. The track finding efficiencies 

in the TRD (table 13.2) show a similar behaviour as in the STS. 

Track category Efficiency (%), STS Efficiency (%), TRD 

Reference tracks 94.89 93.32 

All tracks 71.45 86.67 

Extra tracks 23.43 60.50 

Clones 0.01 0.00 

Ghost 4.58 8.00 

Table 13.2: Efficiency of track reconstruction in the STS and TRD subdetectors for central Au+Au collisions at 

25 AGeV


It has to be noted that the implementation of the track finding algorithms is strongly dependent on the 

detailed detector geometry and layout. There can be, for instance, two different versions of the algorithm 

working in projections or in space. The first version will create track candidates in projections and then 

combine them into space tracks. In the second approach the algorithm creates space points and then 

combines them into tracks. The track finding algorithm presented here finds tracks working directly with 

space points, thus emulating pixel detectors. The algorithms will be further developed according to the 

STS and TRD design choices. 

13.1.3 Conformal Mapping 

One of the standard methods of track recognition in the magnetic field is the method of conformal mapping 

[220]. It is based on a hit coordinate transformation simplifying the problem of pattern recognition. 

In a homogeneous magnetic field charged particles follow a circular path in the bending plane. This 

assumption could be used as a simplest approximation for CBM dipole magnet field. In the conformal 

mapping method, the circular equation of the particle trajectory 

(x − a) 2 + (y − b) 2 = R 2 

is transformed into a straight line with the prescriptions: 

If one fixes R by imposing the relation 

one obtains a straight line of the form: 

u = x 

x2 y 

, v = 

+ y2 x2 . 

+ y2 a 2 + b 2 = R 2 

v = 1 

− ua 

2b b 

(13.3) 

(13.4) 

This straight line can then be used for pattern recognition by establishing a search road following the 

points, to find out which points belong to a given straight line. A fit to such a straight line in (u,v) space 

will then yield the coordinates of the center of the circle, a and b, and together with eq. 13.4 the radius R 

is also determined. 

A track finding procedure for STS is based on a fast algorithm [221] with conformal mapping transformation 

of the hits (CM track finder). The track finding efficiency of this algorithm is shown in fig. 13.7. 

It varies from 70 to 98% for momenta greater than 1 GeV/c. The efficiency for particles with lower 

momenta could be improved by further tuning of the STS layout and parameters of the algorithm. 

13.1.4 3D track-following method 

The track-following method reconstructs tracks based on the hits measured in the STS tracking stations. 

The algorithm should be stable with respect to initial vertex coordinates and the STS geometry. We used 

some approaches known from [222].


Efficiency 

0.4 

1 

0.8 

0.6 

0.2 

0 

0 5 10 15 20 25 30 

Moment, GeV/c 

Figure 13.7: Track finding efficiency of the CM track finder as function of track momentum 

Figure 13.8: Prediction and search in XoZ projection 

Figure 13.9: Prediction and search in YoZ projection 

The track recognition procedure is accomplished in 3D space on both x-z and y-z projections simultaneously. 

The procedure alternates between both views, predicting a track position on the next station and 

searching for hits in the vicinity of the predicted position. 

Starting from the middle of the target area (see Fig. 13.9), this point is sequentially connected with all hits 

in the first station in y-z view, where tracks are close to straight lines. The straight lines driven via these


two points are prolonged to the plane of the second station. All hits in an asymmetrical corridor around 

the intersection point are then used for fitting a parabola in x-z view (fig. 13.8) which is prolonged to the 

next station. Since several prolongations can happen, we set aside corridors around each point predicted 

on the third station. A similar corridor is set in the y-z view on the third station. If hits are found in this 

limits, they are attached to the track. The method continues the track prolongation and searching for hits 

in corridors around the predicted position towards the outer stations. 

600 

500 

400 

300 

200 

100 

0 

-0.04 -0.03 -0.02 -0.01 0 0.01 0.02 0.03 0.04 

Figure 13.10: Difference between predicted and true 

hit x position in the 4th station 

1200 

1000 

800 

600 

400 

200 

0 

-1.5 -1 -0.5 0 0.5 1 1.5 


hit x position in the 7th station 

1600 

1400 

1200 

1000 

800 

600 

400 

200 

0 

-0.05-0.04-0.03-0.02 -0.01 0 0.01 0.02 0.03 0.04 0.05 


hit y position in the 4th station 

2500 

2000 

1500 

1000 

500 

0 

-0.2 -0.15 -0.1 -0.05 -0 0.05 0.1 0.15 0.2 


hit y position in the 7th station 

The obvious weakness of this approach consists of its dependence on the vertex initialization. This results 

in only 10 % of the secondary tracks to be reconstructed. The track finding efficiency for secondary tracks 

will be improved by removing hits already attached to found tracks and starting the algorithm from vertex 

positions other than the main vertex. 

An essential ingredient of the method is the careful calculation of corridor widths depending on the 

station number and the sign of the particle charge. These parameters were obtained by Monte-Carlo

simulations of typical heavy-ion events in the STS. Since the conventional approach to use symmetrical 

3σ corridors had failed being too inefficient, we use the distributions of deviations between real hit 

positions and their predictions by prolonging straight line or parabola for all seven stations. Examples of 

these distributions are shown in figs. 13.10-13.13. 

First results with this algorithm give a track finding efficiency of 91 – 94 % for track momenta greater 

than 1 GeV, almost independent on the momentum. The average number of ghost tracks, having at least 

one wrongly attached hit, is 1.6 per event. Further improvements rely on a reduction of the corridor 

widths by usage of a more realistic track model and/or the Kalman filter technique. 

13.2 Track fitting 

13.2.1 Kalman filter – first approach 

A track fitting procedure used for the reconstruction of the track parameters is based on the Kalman filter. 

The Kalman filter technique has several advantages: 

• treatment of multiple scattering and energy loss locally by including a noise term; 

• inversion of a matrix with the dimension of the measurement; 

• in its smoother version it gives the best estimate at all measurement positions. 

Here we follow the implementation of the Kalman filter as it is described in [216, 217, 223, 224]. 

In general, the Kalman filter addresses the problem of trying to estimate the state vector R of a discretetime 

process that is gouverned by the linear stochastic difference equation [225] 

Rk = Ak−1Rk−1 + νk−1, k = 1,...,N, (13.5) 

where the matrix Ak−1 relates the state at step k − 1 to the state at step k, {ν} is the process noise – a 

sequence of independent Gaussian variables which can, for example, account for the influence of multiple 

scattering on the state vector. The input information for the filter is a sequence of measurements {u} 

described by a linear function of the vector R 

uk = HkRk + ηk, k = 1,...,N, (13.6) 

where the matrix Hk in this so-called measurement equation relates the state to the measurement uk. Here 

ηk, representing the measurement error, is a sequence of Gaussian random variables with the covariance 

matrix Vk. 

The state vector is 

R = (x, y, tx, ty, q/p) T , (13.7) 

where tx = px/pz, ty = py/pz, p is the momentum and q the charge of the track. 

Three procedures for the propagation of track parameters have been implemented: 

1. the linear extrapolator [216, 217] for fast propagation in field free regions; 

2. the parabolic extrapolator [223, 224] for fast estimation of the track parameters in the magnetic 

field; 

3. the fourth order Runge-Kutta extrapolator [223, 224] for propagation in the inhomogeneous magnetic 

field.


Currently measurements of pixel and strip detectors are implemented in the fitting procedure. The track 

projection matrix for a pixel detector is straightforward. For a strip measurement with a stereo angle α 

the matrix H is given by: 

H = (cosα, sinα, 0, 0, 0). (13.8) 

Thus, a subdetector (here STS or TRD) can be treated as a mixture of pixel and strip detectors. 

For the description of material present in the detector we use an approximate approach since tracing 

a trajectory through the full description of the detector is prohibitive in CPU. All surfaces used in the 

fit are described in a simplified form of cylinders, cones or planes. They are divided into three types: 

measurement surfaces, material surfaces, and extrapolation surfaces, at which the track parameters are 

required for future use. 

The track fit algorithm first provides an approximate trajectory, which is extrapolated to all surfaces to 

find intersections and to order and store all crossed surfaces. These ordered surfaces together with the 

hits are then used to modify the track parameters and covariance matrix taking into account multiple 

scattering and energy loss when a material surface is crossed [226, 227]. 

25000 

20000 

15000 

10000 

5000 

Constant 2.504e+04 

Mean 0.0001572 

Sigma 0.007348 

0 

-0.05 -0.04 -0.03 -0.02 -0.01 0 0.01 0.02 0.03 0.04 0.05 

Resolution (p_reco-p_mc)/p_mc 

30000 

25000 

20000 

15000 

10000 

5000 

Constant 3.028e+04 

Mean 0.01027 

Sigma 1.208 

0 

-10 -8 -6 -4 -2 0 2 4 6 8 10 

Pull q/p 

Figure 13.14: Residuals and normalised residuals of the estimated track momentum at the track vertex z-position 

obtained from 1000 events of central Au+Au collisions at 25 AGeV in the asymmetric inhomogeneous magnetic 

field 

Table 13.3 and figure 13.14 show residuals and normalized residuals (pulls) of the track parameters at 

the track vertex z-position obtained from 1000 events of central Au+Au collisions at 25 AGeV in the 

inhomogeneous magnetic field. The Monte Carlo simulated tracks were transported trough the STS 

subdetector and their positions were smeared with σ = 10 µm in order to emulate double sided strip 

detectors with orthogonal strips in x and y direction. An ideal pattern recognition has been used to 

test the quality of the fitting procedure. All tracks having crossed at least four detectors are used for 

histogramming. 

Parameter Resolution Pull 

x 23 µm 1.05 

y 19 µm 1.00 

tx 0.63 mrad 1.02 

ty 0.60 mrad 1.00 

p 0.73 % 1.21 

p pv 0.63 % 1.20 

Table 13.3: Resolutions and pulls of the fitted track parameter distributions at the track vertex z-position. The 

momentum resolution is given without (p) and with (p pv ) constraint to the primary vertex

13.2. Track fitting 313 

A measure of the reliability of the fit are the pull distributions of the fitted track parameters. The reconstructed 

track parameters and covariance matrix at the vertex where the track originates are obtained by 

propagating the track parameters at the measurement position closest to the vertex, taking into account 

the remaining material traversed. 

Table 13.3 shows the RMS of the Gaussian fits to the residual and normalised residual distributions. All 

pulls are centred at zero indicating that there is no systematic shift in the reconstructed track parameter 

values. The distributions are well fitted using Gaussian functions with small tails caused by the various 

non-Gaussian contributions to the fit. The q/p pull shows slightly underestimated errors. This can be a 

result of several approximations made in the fitting procedure mainly in the part of material treatment. 

It should be noted that the pulls of the slopes parameters tx, ty are most sensitive to multiple scattering, 

while the pull of the inverse momentum is sensitive also to the treatment of energy loss. The momentum 

estimation is influenced also by the accumulated error of track propagation in the magnetic field of the 

subdetector. After the primary vertex is reconstructed the tracks can be refitted with additional constraint 

to the primary vertex position. This improves considerably the average track momentum resolution to 

0.63%. 

δp/p (%) 

2.0 

1.5 

1.0 

0.5 

0 

0 1 2 3 4 5 6 7 8 9 10 

p (GeV/c) 

Figure 13.15: Average momentum resolution as a function of p 

In table 13.3 the average momentum resolution δp/p for reconstructed tracks is shown to be 0.73%. 

There are two main contributions to the momentum resolution [225]: 

• the error due to multiple scattering is given by δp/p ∼ 1/β, which for relativistic particles is almost 

constant; 

• the error due to the position measurement is proportional to p, i.e. δp/p ∼ p. 

Minor effects, like imperfect track propagation in the inhomogeneous field and intrinsic features of the 

fitting routine influence the momentum resolution, especially for low momentum tracks. Figure 13.15 

shows the average momentum resolution as a function of momentum. The resolution is worse for high 

momentum tracks, indicating that the error in the position measurement starts to play a dominant role. 

The next steps in the development of the Kalman track fitter will include simplification and speeding up 

the fitting procedure in order to develop a fast algorithm of fitting tracks for the online event selection.


13.2.2 Kalman filter - second approach 

In a second independent approach, we assume that under the conditions of the inhomogeneous magnetic 

field and multiple scattering of charged particles, the trajectories in the x-z plane 1 could be approximated 

by 

xk = akz 2 k + bkzk + ck, k = 1,...,7, 

where xk is the result of measurement, zk is the z-coordinate of the kth detector. Thus, the fitting trajectory 

will be described by a piecewise curve consisting of parabola fragments 

In order to formulate the Kalman filter algorithm concerning our problem, we determine the state vector 

as 

Rk = (ak,bk,ck) T 

and the measurement vector as 

where ξk is the measurement error and Hk is the matrix 

uk = HkRk + ξk , (13.9) 

Hk = z 2 k ,zk,1 , (13.10) 

To start the Kalman process 13.5, we have to estimate the initial values of the state parameters and the 

covariance matrix of errors related to the initial state,and determine the transformation matrix Ak. The 

initial values of the state parameters are the parameters of a parabola traced through the hits in the three 

first detectors. The covariance matrix is determined by the formula [228]: 

where 

⎛ 

Z = ⎝ 

z 2 1 z1 1 

z 2 2 z2 1 

z 2 3 z3 1 

cov( R) = (Z T WZ) −1 , (13.11) 

⎞ 

⎛ 

⎠, 

⎜ 

W = ⎝ 

1 

ξ 2 0 0 

0 1 

ξ 2 

0 

0 0 1 

ξ 2 

We assume that the transformation matrix Ak has the following form 

Ak = 

⎛ 

⎝ 

f k a 0 0 

0 f k b 0 

0 0 f k c 

⎞ 

⎞ 

⎟ 

⎠. (13.12) 

⎠. (13.13) 

In case of the homogeneous magnetic field Ak is the identity matrix; for an inhomogeneous magnetic 

field Ak may significantly differ from it. To determine its diagonal elements, we use the hits in three 

consecutive detector planes and calculate the parameters of the corresponding parabola ak−1,bk−1,ck−1. 

We then skip the first hit, add the next detector plane and calculate the parameters of this parabola 

ak,bk,ck. Then, 

f k a = ak/ak−1, f k b = bk/bk−1, f k c = ck/ck−1, 

which determines the transformation matrix from the state k − 1 to the state k. The analysis shows that 

the values fa and fb are quit close to unity, while fc significantly differs from unity. 

The Kalman filter algorithm as described above has been applied to central Au+Au events at 25 AGeV 

beam energy, simulated in the CBM STS system. Only tracks with hits in all seven tracking stations 

were considered; ideal track finding and a position resolution of 10 µm were assumed. The preliminary 

results of the Kalman filter are compared in figure 13.16 to those obtained by a least-squares parabolic 

fit. Evidently, the Kalman filter provides a more accurate momentum reconstruction than global fitting 

methods. 

1 The application of the Kalman filter in the y-z plane is analogous to that described here.


120 

100 

80 

60 

40 

20 

0 

-0.1 -0.05 0 0.05 0.1 0.15 0.2 

120 

100 

80 

60 

40 

20 

0 

0.24 0.25 0.26 0.27 0.28 0.29 0.3 

Figure 13.16: Distribution of Δp/p in central Au+Au collisions at 25 AGeV beam energy for tracks with hits 

in all seven STS stations. Left: Results obtained by the Kalman filter; right: results obtained with a least-squares 

parabolic fit. 

13.2.3 Parabolic approximation 

In contrast to the Kalman filter, this global approach to momentum reconstruction is based on the integration 

of the equations of motion in the field and obtains the track parameters by a least squares criterium 

on the hit coordinates. 

The equations of motion of a charged particle in a magnetic field read 

dβ e 

= 

dS Pc [−Hy + tanα(Hz cosβ + Hx sinβ)] (13.14) 

and 

dα e 

= 

dS Pc [Hx cosβ + Hz sinβ], (13.15) 

where P is the momentum, β the azimuthal angle between the projection of the momentum vector on the 

x-z plane and the Z axis, α the deep angle between the momentum vector and the x-z plane, S the track 

length and Hx, Hy and Hz the components of the magnetic field. 

The exact integration of the coupled equations 13.14 and 13.15 is complicated. If, however, |Hy| ≫ |Hx| 

and |Hy| ≫ |Hz|, as is the case in the CBM dipole field, the track can in a 0 th step be approximated by a 

parabolic helix curve and the hit coordinates are described by a parabola in the x-z plane: 

The estimation of the track momentum in the 0 th step is then 

x = az 2 + bz + c. (13.16) 

P0 = 0.3 ¯H 

, (13.17) 

2acosα 

where ¯H = 1 

2 (H1y + HNy) is the mean of the y components of the magnetic field at the first and last 

measured point of the track. 

This momentum estimate serves as input for the 1 st step of the algorithm, which uses a 3 rd order approximation 

for the azimuthal angle: 

β = β0 + ( dβ 

dS )S=0S + 1 

2 (d2 β 

dS 2 )S=0S 2 + 1 

6 (d3 β 

dS 3 )S=0S 3 . (13.18)


The non-uniformity of the magnetic field and the energy loss of the particle are taken into account at this 

step [229]. The value of the momentum is calculated as: 

P1 = P0 + P ′ (H, dH 

dS )S + P′′ (H, dH 

dS , d2 H 

dS 2 )S2 , (13.19) 

where P0 was calculated from equation 13.17 and P ′ and P ′′ are functions of the magnetic field and its 

derivatives. 

Number of particles 

4 

10 

3 

10 

0 1 2 3 4 5 6 7 8 9 10 

p (GeV/c) 

Figure 13.17: Momentum spectrum of charged particles accepted by the STS system in central Au+Au collisions 

at 25 AGeV 

The algorithm was applied to space points of tracks obtained from the transport of 1,000 UrQMD events 

(central Au+Au at 25 AGeV beam energy) through the STS system by the simulation framework CBM- 

ROOT. Ideal track finding was assumed. The space points were smeared in the x and y coordinates 

according to an assumed position resolution of 20 µm. The momentum spectrum of accepted charged 

tracks is shown in figure 13.17. 

Number of tracks 

100 

80 

60 

40 

20 

3 

× 10 

Mean 0.251 

RMS 2.205 

Constant 1.111e+05 ± 231 

Mean 0.02246 ± 0.00153 

Sigma 0.8371 ± 0.0017 

0 

-25 -20 -15 -10 -5 0 5 10 15 20 25 

(p - p ) / p (%) 

reco 

/ p (%) 

p 

σ 

1.8 

1.6 

1.4 

1.2 

1 

0.8 

0.6 

0 1 2 3 4 5 6 7 8 9 10 

p (GeV/c) 

Figure 13.18: (Left) distribution of the relative difference of reconstructed and true momentum for tracks with 

p < 10 GeV/c and a minimum of three hits in the STS. The full line shows a Gaussian fit with a σ = 0.84 %. (Right) 

Relative momentum resolution as function of momentum. The results were obtained for ideal track finding. 

To assess the quality of momentum reconstruction obtained with the algorithm desribed above, the reconstructed 

momenta are compared to the simulated ones. Figure 13.18 (left) shows the distribution of 

the relative difference of reconstructed and true momentum. The mean resolution is 0.84 %; its momentum 

dependence is shown in the right panel of figure 13.18. The resolutions in the angles β and α are



70000 

60000 

50000 

40000 

30000 

20000 

10000 

Mean -0.05287 

RMS 3.778 

Constant 7.15e+04 ± 159 

Mean -0.08521 ± 0.00243 

Sigma 1.202 ± 0.003 

0 

-25 -20 -15 -10 -5 0 5 10 15 20 25 

β - β (mrad) 

reco 

(mrad) 

β 

σ 

1.6 

1.4 

1.2 

1 

0.8 

0.6 

0 1 2 3 4 5 6 7 8 9 10 

p (GeV/c) 

Figure 13.19: (Left) distribution of the difference of reconstructed and true azimuthal angle β for tracks with 

p < 10 GeV/c and a minimum of three hits in the STS. The full line shows a Gaussian fit with σ = 1.2 mrad. 

(Right) Resolution in β as function of momentum. The results were obtained for ideal track finding. 


50000 Mean -0.007802 

RMS 2.313 

40000 

Constant 

Mean 

4.48e+04 ± 114 

0.001443 ± 0.001463 

30000 

20000 

10000 

Sigma 0.6321 ± 0.0019 

0 

-10 -8 -6 -4 -2 0 2 4 6 8 10 

α - α (mrad) 

reco 

(mrad) 

α 

σ 

1.2 

1 

0.8 

0.6 

0.4 

0.2 

0 1 2 3 4 5 6 7 8 9 10 

p (GeV/c) 

Figure 13.20: (Left) distribution of the difference of reconstructed and true deep angle α for tracks with p < 10 

GeV/c and a minimum of three hits in the STS. The full line shows a Gaussian fit with σ = 0.6 mrad. (Right) 

Resolution in α as function of momentum. The results were obtained for ideal track finding. 

shown in figures 13.19 and 13.20. Both angles are reconstructed with an accuracy of about 1 mrad. The 

reconstruction accuracy for all three parameters is worse for low momentum tracks because of the higher 

track curvature and the lower number of STS hits. 


2000 Mean -0.5733 

1800 

RMS 2.185 

1600 

Constant 1885 ± 19.3 

1400 

Mean -0.4112 ± 0.0081 

1200 

1000 

800 

600 

400 

200 

Sigma 0.8606 ± 0.0099 

0 

-10 -8 -6 -4 -2 0 2 4 6 8 10 

(p - p ) / p (%) 

Figure 13.21: Distribution of the difference between reconstructed and true momentum obtained for tracks 

reconstructed by the CM track fitter in central Au+Au collisions at 25 AGeV beam energy. The full line shows a 

Gaussian fit with σ = 0.86 %. 

reco


The algorithm has also been tested with the Conformal Mapping track finder (section 13.1.3). Figure 

13.21 shows the relative difference of reconstructed and true momentum using tracks reconstructed by the 

CM finder. The mean resolution of about 0.86 % is only slightly worse than in case of ideal track finding. 

We conclude that real track finding has only a minor influence on the performance of the momentum 

reconstruction. 

The reconstruction algorithm will be further improved in a 2 nd step, in which the numerical solution 

of the equations of motions will yield more precise values of the track parameters. The results of the 

first step as presented here will serve as input for this second step, which is still under development. 

Preliminary results show that momentum and angular resolutions can be improved by a factor of 1.5 - 2. 

13.2.4 Polynomial approximation 

This algorithm reconstructs the particle momentum directly from the hits in the Silicon Tracking System 

(STS). It consists of two steps. First, the track curve is fitted by a polynomial vector function, using the 

smoothness of the trajectory. Three types of approximation were applied: polynomials, cubic splines and 

B-splines. 

The optimisation problem is described by the residual function 

N 

2 2 ˆx(zi) − xi ˆy(zi) − yi 

F = { 

+ 

}, (13.20) 

i=0 

σ i x 

where xi,yi are the trajectory hits, ˆx(z), ˆy(z) the coordinate approximations, σ i x,σ i y the measurement errors 

and N the number of hits in the tracking system. It should be noted that F is a quadratic functional of 

the parameters describing the coordinate functions ˆx(z), ˆy(z). This means that the optimisation problem 

is reduced to a very fast procedure of multiplication of an a priory prepared matrix with the vectors of hit 

coordinates. 

In the second step, the constructed functions ˆx(z), ˆy(z) are used to determine the approximate value ˆp of 

the momentum. The momentum reconstruction is based on the equations of motion: 

xzz = kq(1 + x2 z + y 2 z ) 1 2 

p 

σ i y 

{xzyzBx − (1 + x 2 z )By + yzBz}, (13.21) 

yzz = kq(1 + x2 z + y2 z ) 1 2 

{(1 + y 

p 

2 z )Bx − xzyzBy − xzBz}, (13.22) 

where p and q are momentum and charge of the particle, respectively, and Bx,By,Bz the components 

of the nonuniform magnetic field in the point (x,y,z). xz, yz and xzz, yzz denote the first and second 

derivatives of x and y with respect to z, respectively. 

With the density function 

f (α,z, ˆx(z), ˆy(z)) = | ˆxzz − αkQ(1 + ˆx 2 z + ˆy 2 z ){ ˆxz ˆyzBx − (1 + ˆx 2 z )By + ˆyzBz}| 2 

+ | ˆyzz − αkQ(1 + ˆx 2 z + ˆy 2 z ) 1 2 {(1 + ˆy 2 z )Bx − ˆxz ˆyzBy − ˆxzBz}| 2 , 

the approximated value ˆp is derived as the inverse of α minimising the functional 

G(α) = 

ze 

zb 

(13.23) 

f (α,z, ˆx(z), ˆy(z)g(z)dz, (13.24) 

where g(z) is a weight function and zb,ze the z coordinates of the first and last STS detector, respectively. 

Since G(α) is a quadratic functional of the parameter α, a fast non-iterative procedure for the evaluation 

of ˆp can be constructed.


Figure 13.22: Distribution of relative momentum residuals using the polynomial approximation algorithm. Left 

column: without multiple scattering; right column: with multiple scattering in the STS. The track approximations 

are polynomial (top row), cubic spline (centre row) and B-spline (bottom row). 

This algorithm has been applied to simulated tracks in the momentum range 1 - 10 GeV/c with hits in 

all seven stations of the STS. Ideal track finding was assumed. The distribution of relative errors with 

and without taking into account multiple scattering in the tracking stations are shown in figure 13.22 

for the three types of track models. The first and second moments of the relative momentuk residual 

distributions are summarised in table 13.4. While in the absence of multiple scattering, the spline ap-


Track model < Δp/p > [%] σp/p [%] < Δp/p [%] σp/p [%] 

no MS no MS MS MS 

polynomial -0.02 0.28 -0.02 0.76 

cubic spline 0.08 0.17 0.09 0.79 

B spline 0.01 0.16 0.00 0.78 

Table 13.4: Mean and RMS of the momentum residual distributions of figure 13.22 

proximations give better results, the performance in the presence of multiple scattering is similar for all 

three approximations (σP = 0.75 − 0.80%). 

13.2.5 Orthogonal polynomial sets 

The method of accurate momentum reconstruction with orthogonal polynomial sets constructs an explicit 

function which gives the momentum in terms of measurable quantities: position and direction of a track 

at the entrance of the spectrometric field and the deflection angle as the effect of the field onto the track 

momentum [234,235,236]. This experimental input information can be provided e. g. by a Kalman filter 

operating on hits registered in the CBM silicon tracking system (STS) (see section 13.2.2). 

In the inhomogeneous magnetic field of CBM, the deflection angle φ, defined between the first and the 

last tracking station, can be expressed as a function of five variables: 

1. (x1,x2): the coordinates of a point in the first STS detector; 

2. (x3,x4): the tangents of the trajectory in the point (x1,x2); 

3. x5 = 1/pc, where p is the momentum and c the speed of light. 

The task is to construct the inverse function 

which provides the accurate momentum restoration [234]. 

p = p(x1,x2,x3,x4,φ), (13.25) 

The procedure consists of two steps. First, the deflection angles for the given magnetic field are calculated 

for a set of representative trajectories. The explicit function 13.25 is then constructed on the basis of these 

trajectories. 

To choose a representative set of trajectories, the experimental ranges [Ai,Bi] of the variables xi are 

normalised to the range [-1,+1] by defining 

gi = 2xi − Ai − Bi 

. (13.26) 

Bi − Ai 

For each variable gi, a discrete number Ni of “points” are choosen according to the Tchebycheff distribution: 

g αi 

i = cos (2αi − 1)π 

, αi = 1,...,Ni. (13.27) 

2Ni 

The numbers N1,N2,...,N5 have to be chosen such that the required accuracy is achieved. They determine 

the collection of fixed trajectories that are traced through the magnetic field (given in form of a detailed 

field map) applying a numerical integration2 . For each trajectory, the deflection angle φ(x1,x2,x3,x4,x5) 

is calculated. 

2 The Runge-Kutta method is used.


Let the range of these deflections be [A6,B6]. We normalise the deflections to the interval [-1, +1]: 

g6 = 2ϕ − A6 − B6 

B6 − A6 

and, by analogy with equation 13.27, choose a discrete number of values of g6 by 

(13.28) 

g α6 

6 = cos (2α6 − 1)π 

, α6 = 1,...,N6, N6 ≤ N5. (13.29) 

2N6 

Now, applying inverse interpolation, we calculate the corresponding values of g5 in the amount equal to 

the product Π 5 i=1 Ni. 

With this set of values, the desired function is constructed by the use of the Tchebycheff polynomials 

defined by 

T0(x) = 1; T1(x) = x; Tn+1(x) = 2xTn(x) − Tn−1(x) (n ≥ 1). (13.30) 

These polynomials form an orthogonal set [237] 

 

Tj(Xα)Tk(Xα) = const · δ jk 

if 

g5 can be expressed in the form 

g5 = 

α 

Xα = cos 

i jklm 

(13.31) 

(2α − 1)π 

, α = 1,...,N. (13.32) 

2N 

Ci jklmTi(g1)Tj(g2)Tk(g3)Tl(g4)Tm(g6), (13.33) 

where the sum runs over i = 0,...,N1 − 1; j = 0,...,N2 − 1; k = 0,...,N3 − 1; k = 0,...,N4 − 1; m = 

0,...,N6 − 1. The coefficients Ci jklm can be calculated taking into account the orthogonality of the 

Tchebycheff polynomials: 

 

Ci jklm = 

α1 

α1α2α3α4α6 

where the sums run over αi = 1,...,Ni. 

α2 

g5α 1 α 2 α 3 α 4 α 6 Ti(g1)Tj(g2)Tk(g3)Tl(g4)Tm(g6) 

( 

Ti(g1)) 2 ( 

Tj(g2)) 2 ( 

Tk(g3)) 2 ( 

Tl(g4)) 2 ( , (13.34) 

Tm(g6)) 2 

α3 

The algorithm described above was applied to the CBM tracking system by computing trajectories for 

the range and number of the variables x1 − x6 as given in table 13.5. The total number of “points” for 

which trajectories were calculated is 625. The deflection angles of all trajectories were calculated by 

transport through the magnetic field. For each combination (α1,α2,α3,α4) and for N6 = 7 values of the 

deflection angle, the momentum variable was calculated by inverse interpolation. The 4,375 expansion 

coefficients Ci jklm were then determined according to equation 13.34. 

To estimate the accuracy of the method, tracks with random values of x1,x2,x3,x4 and p, uniformly 

distributed in their respective ranges, were transported through the magnetic field and the deflection 

angle ϕ was calculated. Then, using the set of coefficients Ci jklm and equation 13.33, the corresponding 

value of the momentum pcim was calculated. The absolute and relative differences between reconstructed 

and true momemtum are shown in figure 13.23. We see that the RMS of the method, which takes into 

account the inhomogeneity of the magnetic field and the spatial distribution of all possible trajectories 

through the spectrometer magnet, is about 0.15%. 

Lowering the upper limits N1, N2, N3, N4, N6, we obtain, without changing the coefficients, a leastsquares 

fit to the computed trajectories. This is a consequence of the Tchebycheff polynomials being 

α4 

α6


Ai Bi Ni 

x1 -1.5 2. 5 

x2 -2. 2. 5 

x3 -0.3 0.44 5 

x4 -0.37 0.37 5 

x5 0.1 1. 7 

x6 0.025577 0.277585 7 

Table 13.5: Ranges [Ai,Bi] and number of “points” for variables x1,x2,x3,x4,x5,x6 

Figure 13.23: Distributions of p − pcim (left, in MeV/c) and p−pcim 

p 

coefficients Ci jklm 

(right), using the complete set of expansion 

orthogonal. The number and significant coefficients can be found by a Fisher test, giving the order of 

the polynomials above which the RMS of the residuals does not decrease significantly. Our analysis has 

shown that without loss in accuracy, only 89 significant coefficients can be used. 

In order to estimate the accuracy of the method on data close to real events, we applied the algorithm 

to data generated by Monte-Carlo simulations for the reaction Au+Au at 25 AGeV beam energy. Figure 

13.24 presents the distributions of absolute and relative difference between reconstructed and true momentum 

for positively charged tracks. Only the 89 significant coefficiencts were used. The dispersion of 

the distribution p−pc 

p is about 0.26%. 

It must be noted that this result is obtained only for positively charged particles, because the tracing of 

the basic set of trajectories through the magnetic field was realised for such tracks. For a small part of 

tracks (approximately 10%), the parameters of which are out of the ranges of variables x1,x2,x3,x4,x6, 

we used the approximation of the uniform magnetic field. This reduces the overall resolution to about 

0.34 %. 

In summary, the algorithm provides the possibility to reconstruct the momentum of charged particles

13.3. Primary vertex fitting 323 

Figure 13.24: Distribution of p− pc (left, in Mev/c) and p−pc 

p (right) for positively charged particles from Monte 

Carlo events 

registered in the STS system with high accuracy (σp ≈ 0.34%). The accuracy can be further improved by 

separate momentum reconstruction for particles of different charges and by subdivision of the momentum 

range into a intervals. 

13.3 Primary vertex fitting 

13.3.1 Minimisation of track impact parameters 

A straightforward approach to the determination of the primary vertex coordinates exploits the fact that it 

is the origin of the majority of the tracks measured in the STS (see figure 13.1). Therefore, the dispersion 

of the track extrapolation coordinates x and y to a plane of constant z will be minimal for z = zPV . The 

mean values of x and y in the primary vertex z plane then give the coordinates of the primary vertex. 

An algorithm based on this method has been tested for central Au+Au events at 25 AGeV beam energy 

simulated by UrQMD and transported through the STS subdetector by the simulation framework 

CBMROOT. Tracks were reconstructed by the Conformal Mapping track finder (section 13.1.3) and the 

Parabolic Approximation track fitter (section 13.2.3). The dispersion of track impact parameters was 

calculated for three z planes (z = −5 cm, z = 0 cm, z = 5 cm) and parametrised by a parabola, the minimum 

of which defined the vertex z position. The mean values of track impact parameters in this z plane 

were taken as x and y coordinates of the primary vertex. The distributions of the difference between 

reconstructed and true primary vertex coordinates are shown in figure 13.25. A precision of about 10 µm 

in x, 5 µm in y and 20 µm in z is reached. This values can still be improved by the implementation of the 

second step of momentum reconstruction in the parabolic approximation. 

13.3.2 Geometrical fit with Kalman filter 

The algorithm for fitting the primary vertex is based on the Kalman estimator and smoother. The term 

“geometrical fit” means that the vertex fit is performed on tracks without particle identification. 

Given a few hundreds of primary tracks, there is no need neither for a preliminary selection of primary


120 

100 

80 

60 

40 

20 

Mean -1.573 

RMS 9.857 

Constant 128.5 ± 5.8 

Mean -1.412 ± 0.360 

Sigma 9.752 ± 0.277 

0 

-50 -40 -30 -20 -10 0 10 20 30 40 50 

μ m 

120 

100 

80 

60 

40 

20 

250 

200 

150 

100 

0 

-100 -80 -60 -40 -20 0 20 40 60 80 100 

μ m 

50 

Mean -0.794 

RMS 5.437 

Constant 237.5 ± 11.0 

Mean -0.8221 ± 0.1920 

Sigma 5.291 ± 0.159 

0 

-50 -40 -30 -20 -10 0 10 20 30 40 50 

μ m 

Mean -4.312 

RMS 20.25 

Constant 129.4 ± 5.9 

Mean -3.915 ± 0.706 

Sigma 19 ± 0.5 

Figure 13.25: Distributions between reconstructed and true primary vertex coordinates (top left: x, top right: y, 

bottom: z) using the Conformal mapping track finder and the parabolic approximation for track fitting. The full 

lines show Gaussian fits. 

tracks nor for an initial guess of the primary vertex position. The estimates of the track parameters and 

their covariance matrices are extrapolated to the mid z-plane of the target (see details in section 13.2.1). 

After extrapolation the vertex fit is performed considering tracks as straight lines. 

The fit problem is to obtain an estimation of the vertex position V = (xv, yv, zv) using the given set of 

the track parameter estimates. For this purpose every track originating from the primary vertex is treated 

as an independent measurement of the vertex position [230, 231]. The fit is performed with V as desired 

unknown vector and the track estimates Tk as known measured quantities. 

To construct the vertex fitting procedure we need to know the dependence of the track parameters on the 

vertex position. Since all tracks are linearised at z = z0, the track model reads 

x(z) = x0 +tx · (z − z0), y(z) = y0 +ty · (z − z0) . (13.35) 

Let the kth track originate from the vertex with unknown slopes (ak, bk). The model of the kth measurement 

can then be written as: ⎛ 

⎞ 

xv − ak(zv − z0) 

⎜ 

⎟ 

⎜ yv − bk(zv − z0) ⎟ 

Tk = ⎜ 

⎟ 

⎜ 

⎟ 

⎝ ak 

⎠ + ηk , (13.36) 

bk 

where Tk = (x0k, y0k, txk, tyk) T is the known vector of the k th track parameter estimates and ηk is an 

unknown vector of errors in Tk. The errors are assumed to be Gaussian with known covariance matrix. 

The model (13.36) is clearly nonlinear in the first and second components and has to be linearised before 

applying the usual Kalman filter formalism. Assuming the estimated vector Rk = (xv, yv, zv, ak, bk) T ,

13.3. Primary vertex fitting 325 

one can obtain the linearised measurement matrix: 

⎛ 

1 

⎜ 0 

Hk = ⎜ 

⎝ 0 

0 

1 

0 

−ak 

−bk 

0 

(z0 − zv) 

0 

1 

⎞ 

0 

⎟ 

(z0 − zv) ⎟ 

0 

⎟ 

⎠ 

0 0 0 0 1 

and the measurement model becomes 

, (13.37) 

Tk = Hk · Rk + ηk . (13.38) 

Note that the matrix Hk depends on unknown quantities. Therefore the whole fit procedure has to be repeated 

a few times using the fitted values zv, ak, bk from the previous iteration. The number of iterations 

is fixed to be three. 

The Kalman filter formalism cannot be directly applied to the constructed model, since the total number 

of unknown quantities (3 + 2 · Ntracks) is large. As the unknown slopes ak, bk are used only in the 

k th measurement and have no further influence on the vertex position, there is no need to store and 

update the corresponding covariance matrix elements; they are therefore used only temporarily as “virtual 

parameters” for the k th measurement. 

Since the last two parameters of the vector R are replaced for each track, the covariance matrix C is 

partially initialised adding every new measurement: 

where inf denotes a large value. 

Ck = 

⎛ 

⎜ 

⎝ 

C11 k−1 C12 

k−1 C13 

k−1 0 0 

C12 k−1 C22 

k−1 C23 

k−1 0 0 

C13 k−1 C23 

k−1 C33 

k−1 0 0 

0 0 0 inf 0 

0 0 0 0 inf 

⎞ 

⎟ 

⎠ 

, (13.39) 

After the vertex fitting at each iteration to improve the track parameters, all used tracks are refitted by the 

same vertex fit procedure but with the vertex parameters V and the vertex part of the covariance matrix 

C being fixed. This step is equivalent to the smoother part of the Kalman filter formalism. 

Parameter Resolution (µm) Pull 

xv 1.07 1.09 

yv 0.87 1.01 

zv 5.15 1.08 

Table 13.6: Residuals and normalised residuals (pulls) of the primary vertex parameters obtained from 

1000 events of central Au+Au collisions at 25 AGeV in the asymmetric inhomogeneous magnetic field 

Table 13.6 gives the precision of the primary vertex reconstruction obtained from 1000 events of central 

Au+Au collisions at 25 AGeV. The algorithm provides a very high accuracy: the residuals of the xv and 

yv positions of the primary vertex are about 1 µm, and the zv position is reconstructed with an accuracy 

of 5 µm. The normalised residuals (pulls) are close to unity. Some increase of the pulls of the xv and zv 

coordinates are probably due to the track propagation error in the bending plane and to the linear track 

model approximation in the vertex region. 

The total number of reconstructed tracks used in the primary vertex fitting routine (Figure 13.26) is quite 

large. In order to investigate the dependence of the vertex resolution on this number, the primary vertex


22 

20 

18 

16 

14 

12 

10 

8 

6 

4 

2 

0 

0 100 200 300 400 500 600 700 800 900 1000 


Figure 13.26: Number of reconstructed tracks per 

event used by the primary vertex fit algorithm for central 

Au+Au collisions at 25 AGeV 

Resolution x v , y v (µm) 

2.0 

1.5 

1.0 

0.5 

0 

0 

0 50 100 150 200 250 300 350 400 450 500 


Figure 13.27: Primary vertex position resolutions 

versus number of tracks used in the primary vertex fit 

(the scale for zv is shown on the right side) 

fitting routine was applied to a randomly selected subset of tracks (Figure 13.27). Such ∼ 1/ √ N type 

behavior allows a significant simplification of the fitting routine in case the maximal precision of the 

primary vertex is not required. This will be especially important for online event selection where time 

consumptions are crucial. 

13.4 Secondary vertex fitting 

13.4.1 Geometrical fit with Kalman filter 

This method of secondary vertex fitting is a straight forward application of the Kalman filter. Similarly 

to the primary vertex fit (see section 13.3.2), the track model is a straight line in the vertex region, but the 

vertex coordinates and track parameters are now treated simultaneously. Thus, the algorithm provides not 

only the vertex position, but also improved estimations of the track parameters, including their momenta. 

Let a vertex V = (xv,yv,zv) be composed of n tracks with slopes ak,bk and momenta pk. The (3 + 3n)dimensional 

state vector R is given by: 

R = (xv,yv,zv, a1,b1, p1, ... an,bn, pn) . (13.40) 

The Kalman filter is implemented in the standard operational form using the whole covariance matrix of 

the state vector R. This standard implementation as well as the use of momenta instead of their inverse 

values allow to incorporate any type of constraints on “vertex-tracks” or “tracks-tracks” parameters into 

the Kalman filter. 

Let the track estimate Tk = (xk,yk,txk,tyk,Pk) T be linearised at z = z0. In the original track estimate the 

signed inverse momentum (q/P)k is measured; therefore the covariance matrix of Tk has to be converted 

to the momentum measurement. The measurement matrix is constructed in the same way as in the 

primary vertex fit where momentum and track slopes are directly measured: 

Hk = 

⎛ 

1 0 −ak 0 0 0 ··· z0 − zv 0 0 ··· 0 0 0 

⎜ 0 1 −bk 0 0 0 ··· 0 z0 − zv 0 ··· 0 0 0 

⎟ 

⎜ 

⎟ 

⎜ 0 0 0 0 0 0 ··· 1 0 0 ··· 0 0 0 ⎟ 

⎜ 

⎟ 

⎝ 0 0 0 0 0 0 ··· 0 1 0 ··· 0 0 0 ⎠ 

0 0 0 0 0 0 ··· 0 0 1 ··· 0 0 0 

⎞ 

x v 

y v 

z v 

15 

10 

5 

Resolution z v (µm) 

(13.41)

13.4. Secondary vertex fitting 327 

Since the matrix Hk contains many vanishing elements, the Kalman filter algorithm can be significantly 

sped up eliminating all operations with zero elements of Hk. The algorithm proceeds track by track and 

finally obtains the estimates of the vertex position and smoothed track parameter estimates composing 

the vertex together with the corresponding covariance matrix. 

Parameter Resolution (µm) Pull 

xv 12.8 1.05 

yv 10.6 1.05 

zv 69.3 1.06 

Table 13.7: Residuals and normalised residuals (pulls) of the secondary vertex parameters obtained from 10 5 D 0 

decays in the inhomogeneous magnetic field 

As an example, table 13.7 shows the residuals and the normalised residuals of the D 0 decay vertex 

parameters. The pulls show that the errors are well estimated. The longitudinal resolution is 69 µm. The 

particle hypotheses (π − , K + ) have been used in the track fit procedure to properly account for multiple 

scattering effects. 

13.4.2 Mass and topological constrained fit with Kalman filter 

Precision of the secondary vertex parameters obtained in the geometrical vertex fit (see section 13.4.1) 

can be improved [231, 232] by taking into account several assumptions on the tracks associated to the 

vertex. 

These assumptions should be expressed in terms of constraints on the state vector parameters. Each 

constraint is an equation on the parameters or is treated as an additional measurement. The equationtype 

constraint can also be considered as a measurement with σeq−constr = 0. Thus, setting σeq−constr to a 

relatively small value, the equation-type constraint can be treated by the Kalman filter as a measurement. 

Since the constraints are applied after the geometrical fit, this implies additional steps of the Kalman 

filter algorithm with the modified measurement matrix and gain. In the algorithm a value of σeq−constr is 

taken to be two orders of magnitude less than the covariance matrix elements after the geometrical fit. 

Such small value is enough to satisfy all the constraints when they do not contradict each other. 

Two types of constraints have been included into the secondary vertex fit: a mass constraint and a topological 

constraint. 

The mass constraint can be applied in the case of one or several combinations of particles in the vertex are 

known to originate from a narrow width mass state. Here we consider the case of a single mass constraint, 

since multiple mass constraints can be treated in a similar way. Following the notations from [231,232], 

let Z be a set of those tracks which are required by the mass constraint to form the invariant mass M. Let 

us define: 

⎛ ⎞ 

⎛ ⎞ 

nk = 

⎜ 

⎝ 

nxk 

nyk 

nzk 

⎟ 

⎠ = 

1 

 

1 + a 2 k + b2 k 

⎜ 

⎝ 

ak 

bk 

1 

⎟ 

⎠. (13.42) 

Px = 

nxk pk, Py = 

nyk pk, Pz = 

nzk pk, (13.43) 

k∈Z 

k∈Z 

k∈Z 

k∈Z 

where mk, nk are the mass and direction of k-th particle. 

k∈Z 

E = 

Ek = 

p2 k + m2 k , (13.44)


Then the mass constraint reads 

M 2 = E 2 − P 2 x + P 2 y + P 22 z . (13.45) 

Taking the partial derivatives of M 2 one can calculate a linearised matrix H of the mass measurement: 

H3k = 

 

2pknzk nxknykPy + nxknzkPz − 1 − n2 

xk 

Px , 

 

 

H3k+1 = 2pknzk nxknykPx + nyknzkPz − 1 − n2 

yk 

Py , 

H3k+2 = 2pkEE −1 

k − 2(nxkPx + nykPy + nzkPz) 

The elements of H that corresponds to k /∈ Z are zeros. 

k ∈ Z . (13.46) 

The mass constraint is used by Kalman filter as an ordinary one-dimensional measurement with the 

measured value M 2 , the error σeq−constr and the measurement matrix H. 

The topological constraint is used to point a mother particle to the (already) known primary vertex. The 

mother track starts at the secondary vertex (xv,yv,zv), has momentum P = (Px,Py,Pz) as defined above 

and has to point to the primary vertex Vpv = (xpv,ypv,zpv) : 

This requirement can be written as a set of three constraints: 

(V −Vpv) × P = 0 . (13.47) 

0 = (zv − zpv)Py − (yv − ypv)Pz , 

0 = (xv − xpv)Pz − (zv − zpv)Px , 

0 = (yv − ypv)Px − (xv − xpv)Py . 

(13.48) 

The constraint can be included directly into the Kalman filter as three independent measurements with 

zero values and errors σeq−constr. As a result, the mother track will point exactly to the primary vertex. 

The primary vertex errors can also be taken into account. The most elegant way to do this is to add the 

primary vertex parameters into the state vector [231, 232]: 

R = (xv,yv,zv, a1,b1, p1, ... an,bn, pn, xp,yp,zp) , (13.49) 

with the primary vertex covariance matrix included into the extended covariance matrix of the state 

vector. 

Three constraints (Eq. 13.48) are added one by one. For the first constraint linearised measurement 

matrix H is : 

H0 = 0 , 

H1 = −Pz , 

H2 = Py , 

··· 

H3k = pknzk((yv − yp)nzknxk − (zv − zp)nxknyk) , 

H3k+1 = pknzk((yv − yp)nzknyk − (zv − zp)(n2 yk − 1)) , 

H3k+2 = −(yv − yp) + (zv − zp)nyk , 

··· 

H3n+3 = 0 , 

H3n+4 = Pz , 

H3n+5 = −Py , 

(13.50)

13.4. Secondary vertex fitting 329 

with (nxk, nyk, nzk) defined above in the mass constraint part. 

The constructed topological constraint is a one-dimensional measurement with the measured value equal 

to 0, the error σeq−constr and the measurement matrix H. 

The other two constraints (Eq. 13.48) are treated in the same way. 

2000 

1800 

1600 

1400 

1200 

1000 

800 

600 

400 

200 

Constant 1843 

Mean 5.114e-05 

Sigma 0.006134 

0 

-0.05 -0.04 -0.03 -0.02 -0.01 0 0.01 0.02 0.03 0.04 0.05 

Resolution (Zsv_reco - Zsv_mc), cm 

2400 

2200 

2000 

1800 

1600 

1400 

1200 

1000 

800 

600 

400 

200 

Constant 2228 

Mean 0.006488 

Sigma 1.058 

0 

-10 -8 -6 -4 -2 0 2 4 6 8 10 

Pull Zsv 

Figure 13.28: Residuals and normalized residuals (pulls) of the secondary vertex z-position obtained from 10 5 

D 0 decays in the inhomogeneous magnetic field by applying the full sequence of the vertex fitting routines: the 

geometrical fit and the fits with mass and topological constraints 

Parameter G G+M G+T G+M+T 

xv 12.8 12.1 2.5 2.4 

yv 10.6 10.5 2.7 2.5 

zv 69.3 63.4 65.1 61.3 

Table 13.8: Accuracy (in µm) of the secondary vertex parameters obtained from 10 5 D 0 decays in the inhomogeneous 

magnetic field by applying different sequences of the vertex fitting routines: the geometrical (G) fit and the 

fits with mass (M) and topological (T) constraints 

Fig. 13.28 and table 13.8 show residuals and normalized residuals of the D 0 decay vertex parameters 

obtained by the full chain of the vertex fitting algorithms. The pull of the secondary vertex z-position 

shows that the vertex parameters are well estimated. The longitudinal resolution is improved comparing 

to the geometrical fit and now equals 61 µm. Particle hypothesis have been used during the track fits to 

properly account for multiple scattering effects, and in the vertex fit procedure to apply the constraints. 

Table 13.8 gives also accuracy of the secondary vertex parameters for different combinations of the 

vertex routines. One can see that the mass constraint mainly improves the z-position of the vertex while 

the topological constraint increases the resolution of the transversal parameters of the vertex. The best 

resolution is reached by applying both, mass and topological, constraints. 

Parameter S S+G S+G+M S+G+T S+G+M+T 

pπ + 0.81 0.78 0.50 0.69 0.43 

pK− 0.82 0.79 0.62 0.71 0.54 

Table 13.9: Relative momentum resolution δp/p (in %) of the secondary tracks obtained from 10 5 D 0 decays 

in the inhomogeneous magnetic field by applying different sequences of the track and vertex fitting routines: 

the standalone (S) track fit, the geometrical (G) vertex fit and the vertex fits with mass (M) and topological (T) 

constraints


Table 13.9 shows the relative momentum resolutions of the secondary tracks at different stages of the 

event reconstruction. The mass constraint secondary vertex fit gives the most significant improvement of 

the momentum resolution for both particles. The best accuracy is reached applying all constraints. The 

difference in the momentum resolution behavior of π + and K − is probably due to their masses. 

The next step will be the application of the secondary vertex routines to physics analysis and detector 

optimization in order to get the maximum event selection efficiency in the online data reconstruction. 

13.5 RICH ring finding 

In chapter 3.2 particle identification with the RICH detector is discussed. While ring-track matching and 

its implication for the efficiency and purity of particle identification with the RICH detector is discussed 

there in detail, methods for ring finding and fitting are subject of this and the next section. 

13.5.1 Track extrapolation 

Ring finding can directly be combined with ring-track matching if the extrapolation of charged tracks to 

the photodetector plane of the RICH detector is used as possible ring center. A ring can then be searched 

for each track, see section 13.6. By definition, only those rings can be correctly identified with this 

method whose track was detected by the tracking system. All rings belonging to primary vertex tracks 

are thus reliably found. However, a lot of fake rings are constructed from background hits or signals 

from different rings due to the high track density, see fig. 13.29 examples (5) and (4), respectively. As 

is discussed in section 3.2 in the context of ring-track matching, a lot of rings from secondary electrons 

are seen in the RICH detector which are not detected by the tracking system. Since the track density in 

the photodetector plane is rather high, a track farther away from a certain ring center will be matched to 

this ring (see example (2) in fig, 13.29). In case more tracks could be matched to one ring, the one with 

the closest distance to the ring center would be taken. A careful cleaning up of the found ring sample is 

therefore essential for this ring finding method and has to be studied in detail before this method could 

be used reliably. Also, double rings being too close are hardly recognized. A removal of hits allocated to 

found rings would help to find these rings. In addition, information from other detectors (TOF, TRD) can 

be used to reject protons, kaons and pions from the track sample which is used for extrapolation. Having 

less tracks, less fake rings are found. 

13.5.2 Hough Transform 

The Hough Transform was studied as an option for a standalone ring finder providing center and radius 

for each ring. It can as well be used to give an estimate of center and radius of the rings which then 

can be used as input for fitting the ring as described in section 13.6. The Hough transform framework 

is often applied to cope with low resolution search of rings. The Hough transform is robust to a certain 

extent concerning topological gaps in rings (semicircles at detector edges) and concerning a high noise 

background [246, 247]. It converts points of the measurement space, i.e. hits, to points in the parameter 

space. In case of circles in the RICH detector the coordinates of the parameter space are the ring centers 

and their radii.

13.5. RICH ring finding 331 

Figure 13.29: Selected region of photodetector plane of the RICH detector. The signals from PMTs are shown 

as while the track extrapolations of all charged particles to the detector plane are plotted as +, the center of a 

found ring is shown as ×. The numbering shows examples for: (1) good candidates, extrapolation and ring center 

lie close together; (2) probably ring from secondary electron, no track extrapolation lies close to the ring center; (3) 

not recognized double ring; (4) + (5) fake rings from combination of background hits or signals from neighboring 

rings. 

Through three arbitrary signal points (xi,yi), i = 1,2,3 a unique circle can be drawn with the center 

and the radius 

xc = 1 

2 

(x2 2 − x2 3 + y22 − y23 )(y1 − y2) − (x2 1 − x2 2 + y21 − y22 )(y2 − y3) 

, (13.51) 

(x2 − x3)(y1 − y2) − (x1 − x2)(y2 − y3) 

yc = 1 (x 

2 

2 1 − x2 2 + y21 − y22 )(x2 − x3) − (x2 2 − x2 3 + y22 − y23 )(x1 − x2) 

(x2 − x3)(y1 − y2) − (x1 − x2)(y2 − y3) 

(13.52) 

 

Rc = (xi − xc) 2 + (yi − yc) 2 . (13.53) 

However, calculating ring center and radius for all 3-point combinations from a typical central Au+Au 

event at 25 AGeV (about 40 rings and signals in 1000 photomultipliers, see section 3.3.4), 1.7 · 10 8 

combinations have to be processed. This requires about 5000 s Cpu-Time on a 2.2 GHz computer. The 

resulting ring centers show a wide distribution in the parameter space and nearly fill the full circle of 

real rings. This effect can easily be understood considering the fact, that ring center and radius from 

neighboring hits are only vaguely determined. Using hits farther away results in a much better precision 

in the determination of the ring centers. Therefore a procedure was developed reducing the number of 

combinations to 1/3 or even 1/5 of the above values by not combining neighboring hits. The resulting 

distribution of ring centers is presented in fig. 13.30. The Cpu-Time necessary to process one event was 

reduced to less than 80 s.


In the future the procedure can be improved by first identifying clusters of hits and estimating the number 

of rings from the number of hits these clusters contain, and only then running the Hough transform on 

these separated clusters. 

Figure 13.30: An example of the distribution of ring centers extracted with the Hough transform (small dots) 

with reduced number of hits (see text). The hits () are shown for one of the photodetector planes for a central 

Au+Au collision at 25 AGeV. 

In the next step of the usual Hough transform strategy, ring centers and radii are determined by the search 

for the most populated places in the parameter space. A simple method would consist of collecting all 

calculated centers (xc,yc) in histograms of proper granularity and defining a cut-off value in the signal 

height to select real ring centers. The radii corresponding to each center can then be found similarly. 

Unfortunately, this method cannot be used here because of the largely different number of hits for rings of 

varying size (see fig. 3.23) resulting in different statistics and signal heights in the proposed histograms. 

Therefore, a contraction mapping method was used in which ring centers are searched for in these (xc,yc) 

histograms by means of averaging over local maxima in various steps. The first iteration includes the 

following steps: 

1. Collection of all ring centers in a two-dimensional histogram with a granularity of 8.4 × 8.4 mm 2 

and computation of the mean values 〈xc〉, 〈yc〉, 〈Rc〉 and ΔR 2 = 〈R 2 c〉 – 〈Rc〉 2 for each of the bins. 

2. For bins containing more than 30 ring centers and ΔR 2 < 0.4 cm 2 neighboring bins are searched. 

A bin with number j is called neighboring to a bin with number i if (〈xc(i)〉 − 〈xc( j)〉) 2 + (〈yc(i)〉 

– 〈yc( j)〉) 2 ≤ 〈Rc(i)〉 2 . The average of all these centers is performed, { xcen[0], ycen[0], Rcen[0] }, 

and used as input for the next iteration. 

The second contraction mapping accomplishes the estimation of centers and radii, this iteration could be 

repeated several times while reducing the granularity of the histogram in each step. 

1. The ring centers extracted from the first iteration (xcen[0], ycen[0]) are collected in a histogram with 

a granularity of 8 × 8 cm 2 and their mean values are calculated in each bin, xcen[1] = 〈xcen[0]〉, 

ycen[1] = 〈ycen[0]〉 and Rcen[1] = 〈Rcen[0]〉. 

2. Again for each center the neighboring bins are searched, here, all within a distance of 3 cm were 

taken. The mean of these centers is calculated, { xcen[2], ycen[2], Rcen[2] } and could be used for a 

next step in the iteration.


Two iterations of the contraction mapping turned out to be enough to estimate parameters for most of the 

rings. After the second and last iteration, all rings were rejected to which less than a certain number of 

hits within a distance of 2-6 cm could be assigned. This range of distances was chosen since the majority 

of rings lies in this region (see fig. 3.14). 

Figure 13.31: Found ring centers (+) and radii after the second iteration of the contraction mapping. The central 

part of this picture corresponds to the fragment shown in fig. 13.29. All single rings are reliably found, however, 

the method has still uncertainties when dealing with overlapping rings. 

All reconstructed ring center and radii are shown in fig. 13.31. They can serve as an input for the ring 

fitting method introduced in section 13.6 which will yield more precise center and radii of the rings. In 

general, it could be shown that for all single rings the method works reliably. Further improvement is 

needed for overlapping rings, semicircles at the detector edges and clusters of noise. Also, due to the 

rejection of rings with less than a certain number of hits, a lower border on the radius of recognized rings 

is given. 

13.5.3 Elastic Net for standalone RICH ring finding 

The elastic net method [238] is a kind of artificial neural network [239, 240] that has been used for track 

recognition in high energy physics [215, 217, 241, 242]. The elastic net algorithm is one of the best 

optimization algorithms in terms of efficiency and speed. 

The method is well illustrated on a simple example of the traveling salesman problem (TSP). The traveling 

salesman problem is a classic problem in the field of combinatorial optimization, in which efficient 

methods for maximizing or minimizing a function of many independent variables is sought. The problem 

is to find for a number of cities with given positions the shortest tour in which each city is visited once. 

All exact methods known for determining an optimal route require a computing effort that increases 

exponentially with the number of cities, so in practice exact solutions can be attempted only on problems 

involving a few hundred cities or less. The traveling salesman problem thus belongs to the large class of 

nondeterministic polynomial time complete problems. Many heuristic algorithms were developed for the 

TSP aiming to bypass the combinatorial difficulties [243]. One of the most successful approaches to the 

problem is the elastic net of Durbin and Willshaw [238]. The elastic net can be thought of as a number of 

beads connected by elastics to form a ring. The essence of the method is to iteratively elongate a circular


close path in a non uniform way until it eventually passes sufficiently near to all the cities to define a 

tour. 

a b dc c 

e f 

d e f 

a b c 

Figure 13.32: Example of the progress of the elastic net method in the traveling salesman problem with 

100 cities [238] 

Following the deformable template approach [239], let us denote the cities byxi. We are going to match 

these cities with template coordinatesya such that 

a |ya −ya+1| is minimum and that eachxi is matched 

by at least one ya. Define a binary neuron sia to be 1 if a is matched to i and 0 otherwise. The following 

energy expression is to be minimized in a valid tour: 

E (sia,ya) = 

sia · |xi −ya| 2 + γ · 

|ya −ya+1| 2 . (13.54) 

ia 

The multiplier γ governs the relative strength between matching and tour length. Applying the mean field 

approximation [239] one can derive the dynamical equation: 

 

Δya = η 2 

 

via · (xi −ya) + γ · (ya+1 − 2ya +ya−1) , (13.55) 

where continuous neurons via describe matching of a to i: 

i 

a 

via = e−|xi−ya| 2 /T 

 

b e−|xi−yb| 2 . (13.56) 

/T 

Here the “temperature” T is decreasing at each update of templatesya, and η is the parameter controlling 

the minimization speed. 

The algorithm is thus a procedure for the successive recalculation of the positions of a number of points 

of the plane in which the cities lie. The points describe a closed path which is initially a small circle 

centered on the middle of the distribution of cities and is gradually elongated non-uniformly to eventually 

pass near all the cities and thus define a tour around them, see Fig. 13.32 (for details see the original 

paper [238]). Each point on the path moves under the influence of two types of force (see Eq. 13.55): 

1. the first moves it towards those cities to which it is nearest; 

2. the second pulls it towards its neighbors on the path, acting to minimize the total path length. 

By this process, each city becomes associated with a particular section on the path. The tightness of the 

association is determined by how the force contributed from a city depends on its distance, and the nature 

of this dependence changes as the algorithm progresses. Initially all cities have roughly equal influence 

on each point of the path. Subsequently a larger distance becomes less favored and each city gradually 

becomes more influenced by the points on the path closest to it. 

The elastic net algorithm produces tours of the same quality as other well known heuristic algorithms [244].


Evolution of the elastic net coordinates in continuous space results in significantly large number of iterations 

without changing the order of the cities. This can be avoided if the net nodes will force to coincide 

with cities at each iteration. Such modification of the elastic net has been developed by us in order to 

increase speed of the net. In this so-called discrete algorithm the elastic net can be represented as a 

closed tour passed exactly through a subset of the cities. An iteration consists of adding new cities to or 

releasing some cities from the net. 

File name Number of cities Extra path (%) Time, ms Time per city, µs 

berlin52 52 0.00 0.98 19 

st70 70 4.27 1.27 18 

kroA100 100 3.03 1.46 15 

lin105 105 0.78 1.84 18 

ch130 130 5.59 2.56 20 

tsp225 225 5.34 4.36 19 

pcb442 442 8.37 12.35 28 

pr1002 1002 6.12 24.94 25 

pr2392 2392 8.42 58.53 24 

Table 13.10: Extra path length (in % to the optimum) and time (in ms) of the discrete ENN algorithm in the TSP 

problem for several distributions of cities with known optimal tour 

Results of application of the discrete ENN algorithm to several distributions of cities with known optimal 

tour are presented in Table 13.10. The algorithm has good performance for real-life applications and is 

extremely fast. Numbers in the last column of the table show almost constant execution time per city 

that means linear behavior of the algorithm with respect to increase of the combinatorial complexity of 

the problem. 

Based on the discrete elastic net we have developed an algorithm for standalone RICH ring reconstruction. 

The algorithm has been already successfully tested on RICH data of the COMPASS experiment 

[245]. Here we focus on maximizing the speed of the algorithm aiming its implementation in the 

Level-1 trigger. The task of ring finding in the RICH detector with about 1000 hits per event is similar in 

combinatorial complexity to the pr1002 example in the table 13.10. Having now additional knowledge 

of the form of the final tour one can expect increase of the speed down to a few milliseconds per event. 

The task is to reconstruct rings by measured hits on a detection plane of the RICH detector. As circular 

form of rings is predefined there is no need in internal forces of the elastic net. In contrast to the TSP 

problem here the net does not pass through all hits. The task is to find rings surrounded by maximum 

hits within errors of measurements. The interaction force between hits and the net does not depend on 

distance. In this case noise hits do not attract the net making the algorithm robust. The net converges to 

the area of maximum condensation of hits within 2–3 iterations, therefore the total time to reconstruct an 

event is proportional to: 

TL1ENN ∼ Nrings · Nhits per ring. (13.57) 

Since the rings are independent, the algorithm reconstructs them one by one 3 . The elastic net algorithm 

performs searching for a ring in a local area of the detector plane. When the ring found by the elastic net 

is accepted, all hits belonging to the ring are marked as used. The algorithm repeats until it recognizes 

rings among hits left on the plane. Rings are fitted at the step of searching. Example of a reconstructed 

event is given in Fig. 13.33. 

The performance of the L1ENNRingFinder algorithm is presented in Table 13.11. The “all set” contains 

3 In a hardware implementation the algorithm can run in parallel several elastic nets.


Figure 13.33: Example of a RICH event reconstructed by the L1ENNRingFinder algorithm. Upper and bottom 

parts of the RICH detector are shown separately. Reconstructed rings are blue, while Monte Carlo rings are drawn 

in red color (with slightly scaled radius to avoid overlapping with the reconstructed rings). 

Rings set Performance (%) Number of rings 

Reference set efficiency 92.21 1425 

All set efficiency 80.52 4179 

Extra set efficiency 74.47 2754 

Clone rate 3.26 142 

Ghost rate 14.98 652 

Found MC rings/event 33 

Time/event (ms) 1.07 

Table 13.11: Performance of the L1ENNRingFinder algorithm taken on 100 events of central Au-Au collisions 

at 35 AGeV 

rings with more than 5 hits. In the “reference set” we put rings which originate from the target region 

and have more than 15 hits. The other rings form the “extra set”. 

A reconstructed ring is assigned to a generated Monte Carlo ring if there is at least 70% hits correspondence. 

A Monte Carlo ring is regarded as found if it has been assigned to at least one reconstructed 

ring. If the ring is found more than once, all additionally reconstructed rings are regarded as clones. A

13.6. RICH ring fitting 337 

reconstructed ring is called ghost if it is not assigned to any Monte Carlo ring using 70% criteria. 

The L1ENNRingFinder algorithm shows a very good efficiency for reference rings (92%). Ghost rate 

will be suppressed at the next step when rings will be matched to tracks from other detectors. 

Because of its computational simplicity and extremely high speed (1 ms), the algorithm is considered to 

be further implemented in hardware which can increase the speed by another few orders of magnitude. 

13.6 RICH ring fitting 

The ring fitting methods presented in this section could come as a second step after ring finding if center 

and radius were not yet precisely determined by the ring finding algorithm. The next step towards particle 

identification with the RICH detector, ring-track matching, is discussed in chapter 3.2. 

13.6.1 Robust ring fitter 

The RFit algorithm presented in this section was developed using experience from the CERES/NA45 

experiment at CERN and needs the approximate center and ring radius as input. Both can be taken from 

any of the methods described in the previous section 13.5. The method has already been implemented in 

the CBM simulation framework cbmroot [62]. 

RFit implements two approaches estimating the ring parameters from a set of signal points (hits) measured 

by the RICH detector. Both of them require, besides hits, rough values of circle center coordinates 

and radius (ring guidance) as an input. The first approach is based on the least square fit of circles and 

circular arcs. This fit can be defined as the following minimization problem. Given n points (xi,yi), 

1 ≤ i ≤ n, the objective function is defined by 

V ≡ σ 2 n 

= d 2 i /n, (13.58) 

where, di is the geometric distance from the point (xi,yi) to the circle, 

 

 

di = 

(xi − x0) 2 + (yi − y0) 2 

 

− r 

, 

(x0,y0) is the ring center, r is its radius. The minimization of V is a nonlinear problem that has no closed 

form solution, therefore all known algorithms for finding the minimum of V are either approximative or 

iterative and, hence, time consuming. The RFit algorithm uses the MINUIT package for minimization, 

more precisely, the ROOT::TMinuit class is implemented. Values of (x0,y0) and r providing the 

minimum to V are taken as coordinates of the searched ring center and its radius. 

The second approach is a generalization of the first one using the so called robustness [248, 249, 250]. It 

should be used for data with background and overlapping rings. Actually, it is the previous least square 

procedure enlarged only with a special weight function which is iteratively changed during calculations. 

Now, in place of (13.58), the function to be minimized is taken in the form: 

V ≡ σ 2 n 

= wid 2 n 

i / wi, (13.59) 

where weights wi are so-called Tukey’s bi-weights [251], 

wi = 

⎧ 

⎨ 

⎩ 

i=1 

 

1 − d2 i 

c 2 T σ2 

i=1 

i=1 

2 

, di < c T σ, 

0, otherwise. 

(13.60)


It is clear that due to Tukey’s bi-weights the contribution of hits to V decreases as their distance, di, from 

the ring increases. Besides, the most distant points (di > c T σ) are ignored at all. The strategy of ring 

search is similar to the simulated annealing scheme [252] with c T playing the role of a temperature. By 

setting c T to a big initial value in the first step, almost all hits are taken into account in a given vicinity of 

a guidance point. After finding the first rough image of the ring by minimizing V , it is gradually refined 

by a stepwise decrease of c T , and V is minimized with this smaller c T . Here, the MINUIT package 

is enabled for minimization, too. As a result of this “annealing” process the required circle turns out 

to be placed near the global minimum of the “potential” V after several iterations of the “temperature” 

decrease. The minimum “temperature” to which c T should drop is defined by statistical fluctuations of 

the ring radii. 

In the CBM simulation framework cbmroot estimates for ring center and radius were used as input 

for the CbmRichRingFinder class resulting in a set of fitted rings around these guidances wherever 

possible: 

• For each guidance only hits which lie within a distance of 1.5rguidance, rguidance = 5 cm, or any 

other preliminary estimation, are taken with weights wi = 1. All other hits are neglected by setting 

wi = 0. If the number of hits taken into account is less than some threshold value, the guidance 

under consideration is neglected. The threshold can be user defined, otherwise the default value 3 

is taken. 

• Calculation of ring parameters using RFit algorithm, i.e. setting cT = 5 as the initial value and 

performing the following loop while cT > 1: 

– calculate wi using the Tukey function and the current values of σ and cT ; 

– minimize V varying (x0,y0) and r; 

– reduce cT by a factor of two: cT / = 2. 

• If the ring radius is larger than a · relectron, the ring is considered as fake and rejected. 

• If several guidance points belong to the same ring, i.e. the distance between both guidances is less 

than the ring radius, their parameters are compared. The guidance which gives the smallest σ is 

taken, that means the one being closest to the fitted ring center. 

A result of CBM RICH simulation data processing by the robust fitting algorithm is shown in Fig. 13.29. 

As can be seen, the precision for type (1) rings is very good. A small study (61 UrQMD events, central 

Au+Au collisions at 25 AGeV) has shown that for e ± and π ± coming from the primary vertex the ring 

fitting method was successful with close to 100% efficiency. The precision of the ring radius fitting has 

been found to be about 2 mm, or 4% in case of the electron rings. 

Further improvements of this algorithm should cover the problem of ring fitting in case of close and 

overlapping rings. Also possible distortions of the rings to ellipses due to the imaging properties of the 

mirror (see fig. 3.12) should be taken into account. A replacement of MINUIT by a specialized algorithm 

could save computing time.

14 Hadron identification 

For the identification of primary hadrons in CBM, the time-of-flight (TOF) detetcor system will be used. 

In this section, we study its ability for identification of charged pions, kaons and protons. The TOF 

system is located at approximately 10 m downstream of the target. For the results discussed below, we 

assume a time resolution of 80 ps (see chapter 5). 

14.1 Acceptance for TOF identified particles 

Since the TOF system covers approximately the same angular acceptance as the STS, the requirement 

of a hit in TOF does not result in a restriction in the acceptance region. The acceptance probability, 

however, is reduced for unstable particles due to decay-in-flight between STS and TOF. 

(GeV/c) 

t 

p 

2 

1.8 

1.6 

1.4 

1.2 

1 

0.8 

0.6 

0.4 

0.2 

π+ 

0 

0 0.5 1 1.5 2 2.5 3 3.5 4 

rapidity 

(GeV/c) 

t 

p 

2 

1.8 

1.6 

1.4 

1.2 

1 

0.8 

0.6 

0.4 

0.2 

+ 

K 

0 

0 0.5 1 1.5 2 2.5 3 3.5 4 

rapidity 

(GeV/c) 

t 

p 

2 

1.8 

1.6 

1.4 

1.2 

1 

0.8 

0.6 

0.4 

0.2 

p 

0 

0 0.5 1 1.5 2 2.5 3 3.5 4 

rapidity 

Figure 14.1: Acceptance for positively charged hadrons in central Au+Au collisions at 25 AGeV 

Figure 14.1 shows the geometrical acceptance for primary pions, kaons and protons, requiring at least 

four hits in the STS and a hit in a TOF detector. The overall acceptances, calculated for central Au+Au 

events at 25 AGeV and p+A events at 80 AGeV with the UrQMD transport code, are listed in table 14.1. 

Its dependence on beam energy is shown in figure 14.2. Note that the acceptance calculation was done 

with a fixed magnetic field for all energies. By modification of the field, the acceptances can to some 

extent be optimised for different collision systems. 

reaction particle 

π + π − K + K − p 

central Au+Au at 25 AGeV (%) 39.7 39.4 34.3 35.1 54.4 

p + Au at 80 GeV (%) 27.6 24.9 25.5 32.8 1.6 

Table 14.1: Total acceptance values for two reactions 

14.2 Hadron identification by time-of-flight 

The principle of particle identification by time-of-flight relies on simultaneous measurement of momentum 

and velocity of a particle. Assuming that a track trajectory is reconstructed from the interaction 

vertex to the TOF system, the measured time-of-flight allows to calculate the velocity β of the particle. 

Together with the momentum p also following from track reconstruction, the (squared) mass of the 

339


acceptance (%) 

65 

60 

55 

50 

45 

40 

35 

30 

25 

20 

π+ 

+ 

K 

10 15 20 25 30 

Au beam momentum (GeV/nucleon) 

Figure 14.2: Total acceptance in central Au+Au collisions as a function of the beam momentum for different 

hadron species 

particle can be calculated as 

m 2 = p 2 1 

 

− 1 

β2 p 

(14.1) 

In most cases, the TOF resolution σt dominates the error in the squared mass over the contributions of 

momentum and track length inaccuracies. Then, the error in m 2 

σ m 2 = 2p2 

β 2 

σt 

t 

(14.2) 

is independent on m and is a quadratic function of the momentum. Because of this quadratic dependence, 

the PID capability quickly decreases with increasing momentum as shown in figure 14.3 for positively 

charged particles. In the following, we concentrate on the separation of kaons from pions, since protons 

can be more easily identified due to the larger mass difference. 

For the quantitative PID, the two-dimensional probability density function (PDF) has to be derived as the 

sum of the single particle functions: 

PDF(p,m 2 ) = 

PDF (α) (p,m 2 ) , (14.3) 

α 

where the summation runs over the contributions of pions, kaons and protons. The single particle function 

can be written as 

PDF (α) (p,m 2 ) = rα(p) fα,p(m 2 ) , (14.4) 

where rα(p) is the momentum spectrum of particle α after the detector acceptance. For illustration, we 

assume a purely Gaussian contribution of each particle with a momentum dependent width: 

fα,p = 

1 

√ 

2πσm2(p) exp 

 

− (m2 − m2 α) 2 

2σ2 m2 

(14.5) 

It should be mentioned that this ansatz implies an uniform resolution over the full active volume of the 

TOF system without any dependence of the detector response on the particle species (specific energy 

loss). More complicated response function can, however, be incorporated in a similar fashion.

14.2. Hadron identification by time-of-flight 341 

(GeV/c) 

lab 

p 

10 

9 

8 

7 

6 

5 

4 

3 

2 

1 

-0.2 0 0.2 0.4 0.6 0.8 1 1.2 

2 

2 4 

m (GeV /c ) 

10 

2 

10 

10 

1 

) 

2 

3 

/d(m 

lab 

n/dp 

2 

Figure 14.3: Distribution of m 2 versus momentum 

for positively charged hadrons. The distance between 

target and TOF detector is 10 m, the time resolution 80 

ps. 

d 

) 

2 

/ d(m 

lab 

dn / dp 

18 

16 

14 

12 

10 

8 

6 

4 

2 

total 

+ π 

+ 

K 

p 

-0.2 0 0.2 0.4 0.6 0.8 1 1.2 

2 

2 4 

m (GeV /c ) 

Figure 14.4: m 2 spectrum for positively charged particles 

at plab = 6 GeV/c. The solid line shows the fit 

to the spectrum, the dotted lines the contribution of the 

single particle species (π,K, p). 

The PDF is constructed by fitting the measured m 2 distribution in momentum bins, in this case by the sum 

of three Gaussians as demonstrated for simulated events in figure 14.4. The result of the parametrisation 

is shown in figure 14.5. 

(GeV/c) 

lab 

p 

10 

9 

8 

7 

6 

5 

4 

3 

2 

1 

-0.5 0 0.5 1 1.5 

2 

2 4 

m (GeV /c ) 

Figure 14.5: Probability density to measure positively charged hadrons (red: pions; green: kaons; blue: protons) 

as a function of squared mass and momentum 

Kaon identification can be achieved on a track-by-track basis by defining kaon candidates by a momentumdependent 

window in the m 2 distribution. Such a procedure is typically used for the reconstruction of 

decays by the invariant mass method, where PID is employed to select decay daughter candidates. The


PDF in this case is used to calculate efficiency and purity of the selection. Figure 14.6 shows the resolution 

in m 2 as a function of momentum for the chosen set of parameters. It can be seen that below 

3.5 GeV, the ±2σ separation of kaons and pions allows a clean selection of kaons. The efficiency of 

K + identification is shown in figure 14.7 as function of the momentum for selection purities of 50% and 

90%. 

) 

4 

/c 

2 

(GeV 

2 

m 

σ 

0.45 

0.4 

0.35 

0.3 

0.25 

0.2 

0.15 

0.1 

0.05 

0 

1 2 3 4 5 6 7 8 9 10 

p (GeV/c) 

lab 

Figure 14.6: Resolution in m 2 as a function of momentum. 

The horizontal line indicates a ±2σ separation 

of pions and kaons. 

efficiency (%) 

100 

90 

80 

70 

60 

50 

40 

30 

20 

purity 90 % 

purity 50 % 

2 3 4 5 6 7 8 9 

p (GeV/c) 

lab 

Figure 14.7: Efficiency of K + identification as a 

function of momentum for the selection purities 50% 

and 90% 

For the measurement of inclusive kaon spectra, the PDF can be used for a statistical unfolding of the 

measured spectrum. For each track, the probability to be a kaon can be calculated from the measured momentum 

and m 2 according to the PDF. Figure 14.8 compares the momentum spectra of pions, kaons and 

protons obtained in this way to the true distributions. The reconstructed midrapidity kaon mt spectrum is 

shown in figure 14.10, again compared to MC truth. With the event statistics used in the simulation (10 5 

central Au+Au events), the method works reliably up to a momentum of 8 GeV. Figure 14.10 shows the 

distributions of identified hadrons in the y-pt plane. The upper momentum cutoff restricts the accessible 

pt range at forward rapidities.

14.2. Hadron identification by time-of-flight 343 

lab 

dn / dp 

10 

2 

10 

1 

π+ 

+ 

K 

0 1 2 3 4 5 6 7 8 9 10 

p (GeV/c) 

lab 

Figure 14.8: Momentum distribution of accepted and 

identified positively charged hadrons. The solid lines 

show hadrons accepted in STS and TOF, the points denote 

the reconstructed distributions. 

(GeV/c) 

t 

p 

2.5 

2 

1.5 

1 

0.5 

π+ 

0 

-1 0 1 2 3 4 5 

rapidity 

220 

0 

2 

d n / dy / dpt 

200 

180 

160 

140 

120 

100 

80 

60 

40 

20 

(GeV/c) 

t 

p 

2.5 

2 

1.5 

1 

0.5 

p 

+ 

K 

dy) at midrapidity 

t 

dm 

t 

dn / (m 

10 

1 

-1 

10 

0 

-1 0 1 2 3 4 5 

rapidity 

0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2 

mt 

(GeV/c) 

Figure 14.9: Transverse mass spectrum of K − at 

midrapidity. The solid line shows the spectrum of accepted 

particles, the points the reconstructed spectrum. 

16 

14 

12 

10 

8 

6 

4 

2 

0 

2 

d n / dy / dpt 

0 

-1 0 1 2 3 4 5 

rapidity 

Figure 14.10: Phase space distribution of TOF-identified hadrons. The two solid lines show the geometrical 

acceptance of the STS and TOF and correspond to the polar angles 2.5 and 27 degrees. The dotted line shows the 

identification limit for K + at p = 8 GeV/c. 

(GeV/c) 

t 

p 

2.5 

2 

1.5 

1 

0.5 

p 

60 

50 

40 

30 

20 

10 

0 

2 

d n / dy / dpt

344 Hadron identification

15 Hyperons 

Hyperons will be measured in CBM by their decay into charged hadrons, which are detected in the STS 

and TOF systems. Table 15.1 shows the properties of the hyperons accessible by this method. 

The feasibility of the hyperon measurement in CBM has been studied in a first step using Monte-Carlo 

simulations under idealised conditions: No magnetic field, ideal track finding, ideal particle identification 

for the decay daughters. Tracks were fitted by straight lines assuming the position resolution in the STS 

to be 10 µm. The momentum resolution was assumed to be 1 %. The study is based on 10 5 central 

Au+Au collisions at 25 AGeV, simulated by UrQMD and transported through the CBM setup. For the 

acceptance, the secondary charged tracks are required to have hits in at least four tracking stations and in 

TOF. 

Mass [GeV/c 2 ] Lifetime cτ [cm] Multiplicity Decay channel BR [%] 

Λ 1.116 7.89 36.6 p + π − 63.9 

Ξ − 1.321 4.91 0.99 Λ + π − 99.9 

Ω − 1.672 2.46 0.02 Λ + K − 67.8 

Table 15.1: Properties of the hyperons accessible through their decays into charged hadrons. The yield of Λ 

contains the contribution of the Σ 0 decay, which is experimentally not separable by the identification method used 

in this section. 

15.1 Λ hyperons 

The geometrical acceptance for Λ, requiring both decay daughters to be accepted in the STS and TOF 

detectors, is shown in figure 15.1. Maximal acceptance (≈ 50 %) is achieved near midrapidity and 

intermediate transverse momentum. The total acceptance, given the original distribution as predicted by 

UrQMD, is 15.9 %. Releasing the constraints of PID, i. e. not requiring the tracks to enter TOF, results 

in an acceptance of 27.3 %. The magnetic field reduces the acceptance to 7.7 % (STS + TOF) and 18.1 

% (STS only), respectively. 

Figure 15.2 (left) shows the invariant-mass distribution for all pπ − pairs. The Λ signal is visible above a 

large combinatorial background made predominantly by primary particles. It is reconstructed by fitting 

a Gaussian on top of a second order polynomial for the background. The assumed momentum resolution 

of 1 % translates into an invariant-mass resolution of 0.86 MeV/c 2 . The signal to background ratio, 

calculated in the range of ±2σm, is about 0.12. 

To reduce the background, the decay topology is exploited by the following geometrical cuts: 

• bpp The impact parameters of protons and pions, defined as the distance of the track extrapolation 

to the interaction vertex in the target plane. This cuts reduces the amount of primary particles. 

• bla The impact parameter of the Λ candidate, defined as the distance of the extrapolation of the 

pair momentum to the interaction vertex in the target plane. This cut reduces random combinations 

but also secondary Λ from Ξ and Ω decays. 

These cuts were optimised with respect to the signal to background ratio by studying the simulated 

distributions of the cut variables for signal and background pairs. The values for S/B, significance and 

345

346 Hyperons 

pairs/0.3 MeV 

1200 

1000 

800 

600 

400 

200 

3 

× 10 

[GeV/c] 

T 

p 

2 

1.8 

1.6 

1.4 

1.2 

1 

0.8 

0.6 

0.4 

0.2 

0 

0 0.5 1 1.5 2 2.5 3 3.5 4 

y 

Figure 15.1: STS+TOF acceptance in the rapidity-pt plane for pπ − pairs from Λ decays. 

0 

1.1 1.105 1.11 1.115 1.12 1.125 1.13 

2 

M [GeV/c ] 

inv 

pairs/0.3 MeV 

3 

× 10 

100 

80 

60 

40 

20 

0 

1.1 1.105 1.11 1.115 1.12 1.125 1.13 

2 

M [GeV/c ] 

inv 

pairs/0.3 MeV 

50000 

40000 

30000 

20000 

10000 

1 

0.9 

0.8 

0.7 

0.6 

0.5 

0.4 

0.3 

0.2 

0.1 

0 

0 

1.1 1.105 1.11 1.115 1.12 1.125 1.13 

2 

M [GeV/c ] 

inv 

Figure 15.2: Invariant pπ − mass for all pairs (left), after blp cut (centre) and after blp+bla cut (right) 

Cut Cut value S/B ratio Significance efficiency 

no cut 0.12 144 1.00 

bpp > 0.10 cm 1.27 483 0.77 

+ bla < 0.02 cm 697 574 0.60 

Table 15.2: Signal to background ratio, significance and efficiency for primary Λ before cuts, after the bpp cut 

and after bpp and bla cuts. The significance corresponds to 10 5 central Au+Au events at 25 AGeV. 

efficiency are given in table 15.2. As figure 15.2 demonstrates, there is almost no remaining background 

after the application of the two cuts. 

Other cut variables like z position of the point of closest approach of the tracks and distance of closest 

approach were also studied but did not further improve the background reduction. 

To reconstruct the original Λ spectrum, the signal was extracted in bins of rapidity and pt. The integral 

over the Gaussian distribution was considered to be the uncorrected Λ yield in the respective phase space 

bin. Corrections for geometrical acceptance, branching ratio and cut efficiency were applied on a binby-bin 

basis. Figure 15.3 shows the corrected transverse momentum spectra in two rapidity bins and the

15.2. Ξ − hyperons 347 

corrected rapidity distribution in comparison to the initial input distributions generated by UrQMD. In 

case of incomplete pt coverage, the transverse spectrum was extrapolated assuming a thermal distribution 

dn 

d pt 

∝ pt e −mt/T 

(15.1) 

to obtain the dn/dy values. The rapidity distribution was fitted by a Gaussian function to obtain the total 

Λ yield to be 36.4, in good agreement with the input yield of 36.6 (see table 15.1). We conclude that 

under the idealised assumptions as described above, the phase space distributions and total yield of Λ 

hyperons can be satisfyingly measured in the CBM setup. The required event statistics of central Au+Au 

collisions is of the order of 10 5 . 

dy 

T 

dn/dp 

ev 

1/N 

0.20 

0.18 

0.16 

0.14 

0.12 

0.10 

0.08 

0.06 

0.04 

0.02 

0.00 

0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2 

p T [GeV/c] 

dy 

T 

dn/dp 

ev 

1/N 

0.45 

0.40 

0.35 

0.30 

0.25 

0.20 

0.15 

0.10 

0.05 

0.00 

0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2 

p T [GeV/c] 

dn/dy 

ev 

1/N 

3.5 

3 

2.5 

2 

1.5 

1 

0.5 

0 

0 0.5 1 1.5 2 2.5 3 3.5 4 

y 

Figure 15.3: Λ phase space distributions in simulated central Au+au collision at 25 AGeV beam momentum. Left: 

pt spectrum for y = 0.8 − 1.0; centre: pt spectrum for y = 1.8 − 2.0; right: rapidity distribution. The histograms 

show the initial distributions, the solid points the reconstructed ones. The full lines show the fit functions as 

described in the text. 

15.2 Ξ − hyperons 

The measurement of Ξ − hyperons in the decay channel Ξ − → Λπ − → pπ − π − requires all three charged 

particles in the final state to be accepted by STS and TOF simultaneously. The geometrical acceptance for 

the Ξ − as function of rapidity and transverse momentum is shown in figure 15.4. The global acceptance 

for Ξ − as generated by UrQMD is 5.5 % (STS+TOF) and 16 % (STS only). Applying the magnetic field 

changes this numbers to 1.9 % (STS+TOF) and 7.7 % (STS only), respectively. This acceptance values 

already contain the Λ → pπ − branching ratio. 

For the reconstruction of the Ξ − , candidates for the Λ decay daughter have to be selected. We apply 

an impact parameter cut of 0.05 cm for protons and pions. This reduces the combinatorial background 

significantly while cutting only marginally into the signal (efficiency for Λ from Ξ − decay 98 %). Λ 

candidates are selected by a ±0.8 MeV window in the pπ − invariant mass. The combined efficiency for 

the Λ selection is 90 %. The invariant-mass distribution for combinations of selected Λ candidates with 

π − is shown in figure 15.5. We use the cuts described in the previous section now for the Λ candidate 

and the π − . Again, the combination of impact parameter cuts on the daughters (blp) and on the pair 

momentum (bxi) turns out to be the appropriate one. The cut values, signal-to-background ratio and 

efficiency are listed in table 15.3. 

For the reconstruction of the original Ξ − distribution, Ξ − were generated according to the pt and y distributions 

predicted by UrQMD. 10 5 geometrically accepted Ξ − decays were embedded into full UrQMD 

events. The daughter tracks of Ξ hyperons produced by the UrQMD generator itself were removed from 

the events. Taking into account the Ξ − multiplicity, branching ratio and geometrical acceptance, this

348 Hyperons 

pairs/0.4 MeV 

90000 

80000 

70000 

60000 

50000 

40000 

30000 

20000 

10000 

[GeV/c] 

T 

p 

2 

1.8 

1.6 

1.4 

1.2 

1 

0.8 

0.6 

0.4 

0.2 

0 

0 0.5 1 1.5 2 2.5 3 3.5 4 

y 

Figure 15.4: STS+TOF acceptance in the rapidity-pt plane for Λπ − pairs from Ξ − decays 

0 

1.3 1.305 1.31 1.315 1.32 1.325 1.33 1.335 1.34 

2 

M [GeV/c ] 

inv 

pairs/0.4 MeV 

1400 

1200 

1000 

800 

600 

400 

200 

0 

1.3 1.305 1.31 1.315 1.32 1.325 1.33 1.335 1.34 

2 

M [GeV/c ] 

inv 

pairs/0.4 MeV 

250 

200 

150 

100 

50 

1 

0.9 

0.8 

0.7 

0.6 

0.5 

0.4 

0.3 

0.2 

0.1 

0 

0 

1.3 1.305 1.31 1.315 1.32 1.325 1.33 1.335 1.34 

2 

M [GeV/c ] 

inv 

Figure 15.5: Invariant Λπ − mass for all pairs (left), after blp cut (centre) and after blp+bla cut (right) 

Cut Cut value S/B ratio Significance Efficiency 

no cut 0.004 4.5 0.58 

blp > 0.10 cm 0.35 29.4 0.42 

+ bxi < 0.01 cm 1075 36.5 0.17 

Table 15.3: Signal to background ratio, significance and efficiency for Ξ − before cuts, after the blp cut and after 

blp and bxi cuts. The efficiency before cuts is due to the Λ branching ratio and the Λ selection efficiency (see text). 

The significance corresponds to 10 5 central Au+Au events at 25 AGeV. 

corresponds to 1.8 · 10 6 events. The invariant-mass background with the appropriate event statistics was 

obtained by event mixing of fully simulated UrQMD events, again with the Ξ signal removed from the 

track sample. This background was then added to the mass spectrum obtained from the events containing 

accepted Ξ − . This procedure provides sufficient statistics for the double differential extraction of Ξ − 

yields without the generation and transport of too many UrQMD events, which is CPU-prohibitive. 

The signal is again extracted from invariant-mass spectra in y-pt bins by fitting a Gaussian on top of a 

polynomial background (σm = 1.14MeV/c 2 ). The reconstructed spectra after corrections for acceptance 

and efficiency are compared in figure 15.6 to the original ones. The Ξ − yield is slightly underestimated

15.3. Ω − hyperons 349 

by the method, which is probably due to the Gaussian description of the invariant-mass signal. The 

reconstructed yield (0.856) is 13 % lower than the initial one. 

dn/dpTdy 

1/Nev 

0.010 

0.008 

0.006 

0.004 

0.002 

0.000 

0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2 

p T [GeV/c] 

dn/dpTdy 

1/Nev 

0.016 

0.014 

0.012 

0.010 

0.008 

0.006 

0.004 

0.002 

0.000 

0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2 

p T [GeV/c] 

dn/dy 

1/Nev 

0.12 

0.1 

0.08 

0.06 

0.04 

0.02 

0 

0 0.5 1 1.5 2 2.5 3 3.5 4 

y 

Figure 15.6: Same as figure 15.3, but for Ξ − . The statistics corresponds to 1.8 · 10 6 events. 

15.3 Ω − hyperons 

The Ω − analysis was performed in the same way as for the Ξ − , replacing the secondary π − by K − . The 

geometrical acceptance, defined by requiring STS and TOF hits for all three secondaries, is shown in 

figure 15.7. The overall acceptance is 3.3 %. Releasing the constraint of particles having to reach the 

TOF detector gives an acceptance of 14.6 %. The magnetic field changes this numbers into 1.5 % and 

7.9 %, respectively. 

Λ candidates are selected in the same way as for the Ξ − analysis. The efficiency for the selection is 89 %. 

Figure 15.8 shows the invariant ΛK − mass spectrum before all cuts, after the impact parameter cut on 

the secondaries and after all cuts. The cut parameter values are 0.10 cm for the blk cut and 0.03 cm for 

the bom cut (see table 15.4). As in the case of Λ and Ξ − , the mass spectrum after all cuts is essentially 

background free. The efficiency of the Ω selection cuts is 67 %. 

[GeV/c] 

T 

p 

2 

1.8 

1.6 

1.4 

1.2 

1 

0.8 

0.6 

0.4 

0.2 

0 

0 0.5 1 1.5 2 2.5 3 3.5 4 

y 

Figure 15.7: STS+TOF acceptance in the rapidity-pt plane for ΛK − pairs from Ω − decays 

1 

0.9 

0.8 

0.7 

0.6 

0.5 

0.4 

0.3 

0.2 

0.1 

0

350 Hyperons 

pairs/0.4 MeV 

2200 

2000 

1800 

1600 

1400 

1200 

1000 

800 

600 

400 

200 

3 

× 10 

0 

1.65 1.655 1.66 1.665 1.67 1.675 1.68 1.685 1.69 

M inv [GeV/c^2] 

pairs/0.4 MeV 

9000 

8000 

7000 

6000 

5000 

4000 

3000 

2000 

1000 

0 

1.65 1.655 1.66 1.665 1.67 1.675 1.68 1.685 1.69 


pairs/0.4 MeV 

7000 

6000 

5000 

4000 

3000 

2000 

1000 

0 

1.65 1.655 1.66 1.665 1.67 1.675 1.68 1.685 1.69 


Figure 15.8: Invariant ΛK − mass for all pairs (left), after blk cut (centre) and after blk+bom cut (right) 

Cut Cut value S/B ratio Significance Efficiency 

no cut 0.003 14.4 0.38 

blk > 0.10 cm 2.17 199 0.27 

+ bom < 0.02 cm 642 231 0.23 

Table 15.4: Signal to background ratio, significance and efficiency for primary Ω − before cuts, after the blk cut 

and after blk and bom cuts. The significance corresponds to 3.5 · 10 8 central Au+Au events at 25 AGeV. 

The Gaussian fit to the signal gives an invariant-mass resolution of σm = 1.20 MeV/c 2 . Figure 15.9 

compares the reconstructed distributions in pt and y to the input spectra generated by UrQMD. The 

10 5 simulated Ω − in the detector acceptance correspond, according to multiplicity, branching ratios and 

acceptance, to 1.4·10 8 events. The reconstructed yield (2.29·10 −2 ) is close to the simulated multiplicity. 

dy 

T 

dn/dp 

ev 

1/N 

0.16 

0.14 

0.12 

0.10 

0.08 

0.06 

0.04 

0.02 

-3 

× 10 

0.00 

0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2 

p T [GeV/c] 

dy 

T 

dn/dp 

ev 

1/N 

0.35 

0.30 

0.25 

0.20 

0.15 

0.10 

0.05 

-3 

× 10 

0.00 

0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2 

p T [GeV/c] 

dn/dy 

ev 

1/N 

0.003 

0.0025 

0.002 

0.0015 

0.001 

0.0005 

0 

0 0.5 1 1.5 2 

y 

2.5 3 3.5 4 

Figure 15.9: Same as figure 15.3, but for Ω − . The statistics corresponds to 1.4 · 10 8 events. 

15.4 Conclusions 

The analyses presented in the last sections must be regarded as a first step towards realistic feasibility 

studies for hyperon measurement in CBM. In particular, realistic track finding and fitting in the magnetic 

field will lead to worse results than obtained here by using transport simulations only. The numbers 

given in the previous sections thus have to be regarded as upper limits. However, some preliminary 

conclusions can be drawn. The measurement of Λ, Ξ − and Ω − hyperons in the currently foreseen CBM 

setup is feasible; the setup of the STS system gives rise to a satisfactory acceptance coverage. The 

number of events required to obtain particle spectra and yields is of the order of 10 5 for Λ, 10 6 for Ξ −

15.4. Conclusions 351 

and 10 8 for Ω − . The latter number corresponds to some hours at the foreseen untriggered event taking 

rate. 

Number of events 

220 

200 

180 

160 

140 

120 

100 

80 

60 

40 

20 

0 

1.1121.1131.1141.1151.1161.1171.1181.119 1.12 

Mass, GeV 

Figure 15.10: Reconstructed Λ mass obtained by usage of the parabolic approximation of track fitting. The full 

line shows a Gaussian fit with σm = 1.3MeV/c 2 . 

The next task in this study is to include realistic tracking in the field. First steps in this direction have 

already been taken. Figure 15.10 shows the reconstructed Λ mass using a track fitting routine based 

on the parabolic approximation (see section 13.2.4). A Gaussian fit to the signal gives a mass of mΛ = 

1.116GeV/c 2 and a resolution of σm = 1.3MeV/c 2 .

352 Hyperons

16 Low-mass vector mesons 


Investigation of the invariant mass spectrum of short lived neutral vector mesons (ρ, ω, φ) via detection 

of their electron-positron pairs is one of the major issue of the CBM physics. Besides of the electronpositron 

pairs that originate from the meson decays, there is a huge number of electrons and positrons 

from other physical sources. Among them the Dalitz decays and the gamma conversion in the target 

and detector material budged are the most prominent ones. In addition, the charged pions misidentified 

as electrons contribute to the background. Although dozens of the vector mesons are produced in each 

central nucleus-nucleus collision, the dielectron decay channel branching ratios of the vector mesons are 

very small (see Table 16.1) and sophisticated methods have to be developed to identify real signals. In 

order to verify the feasibility of measurements of the invariant mass spectrum of these mesons a computer 

simulation have been performed. 

Particle N/event Decay BR pairs/event 

π o 372 π o → e + e − γ 1.198 × 10 −2 4.46 

η o 37 η o → e + e − γ 5.0 × 10 −3 0.18 

ω 12 ω → e + e − π o 5.9 × 10 −4 7.1 × 10 −3 

ω → e + e − 7.07 × 10 −5 8.5 × 10 −4 

φ 0.6 φ → e + e − η 1.3 × 10 −4 0.78 × 10 −4 

φ → e + e − 3.1 × 10 −4 1.86 × 10 −4 

ρ 22 ρ → e + e − 4.44 × 10 −5 9.8 × 10 −4 

Table 16.1: Meson multiplicities and their leptonic decay channels included in the analysis. 

16.2 Simulation tools 

• Nucleus-nucleus collision event generator. 

There are no experimental data available on the particle multiplicities for nucleus-nucleus collisions 

at the SIS200 energies. Therefore, the relativistic transport code UrQMD [253] was used as 

the event generator. Our data base contains three sets of simulated Au + Au central collision events 

at 15, 25 and 35 GeV/nucleon. Each set contains 10 5 events. 

• Vector meson decays event generator. 

The PLUTO [254] event generator was applied to simulate the light vector meson decays. The 

input to this code was obtained from the UrQMD simulation and contains the meson rapidity 

and transverse momentum distributions. The multiplicities of the mesons were estimated from 

UrQMD and HSD [256] code and STAR [255] experimental results. Average numbers of e + e − 

pairs produced via the decay of various particles are presented in Table 16.1. 

• Transport code. 

The CBM experimental set up geometry was included into the GEANT3 [257] package from 

353


CERN and the dedicated transport code for CBM experiment was obtained. This package is a part 

of the CBM analysis framework called cbmroot. 

• Light vector meson analysis tools. 

The feasibility study of invariant mass spectrum of short lived neutral vector mesons are performed 

using a package of procedures and codes named CAT (CBM Analysis Tools) which was developed 

by the Kraków group. 

16.3 Background suppression - cut strategy 

In order to suppress the combinatorial background, the following cuts have been applied to single tracks 

or track pairs: 

• The first cut - gamma conversion cut. 

This cut rejects a pair of e + and e − leptons if their closest proximity distance D is smaller than 250 

µm and the invariant mass of this leptonic pair is smaller than 0.1 GeV/c 2 . 

• The second cut - single particle cut. 

This cut is set on each particle (e + or e − ). The lepton is rejected if its track distance from the 

collision vertex is greater than 250 µm and its transverse momentum is smaller than 0.1 GeV/c. 

• The third cut - non-restrictive particle cut. 

This cut rejects e + or e − lepton if pairing this particle with all other unlike sign particles does not 

provide any partner that fulfills all of the following conditions: 

1. The angle between the particle tracks is greater than 10 degrees. 

2. The transverse (vt) and longitudinal (vz) distances of the particle pair vertex from the collision 

vertex are smaller than 150 µm and 1 mm, respectively. 

3. The tracks distance D is smaller than 250 µm. 

• The forth cut - restrictive particle cut. 

This cut is removing the e + e − pair if it does not fulfill any of the following conditions: 

1. The angle between the tracks is greater than 10 degrees. 

2. The transverse (vt) and longitudinal (vz) distances of the particle pair vertex from the collision 

vertex are smaller than 150 µm and 1 mm, respectively. 

3. The tracks distance D is smaller than 250 µm. 

One has to note that the order in which the cuts are set is very important on the background suppression 

result. 

16.4 Results of signal to background ratio studies 

We started our analysis using a parametrized detector acceptance and the following simplifying assumptions: 

• 100% efficiency of electron identification. 

• 1% particle momentum resolution. 

• No magnetic field.

16.4. Results of signal to background ratio studies 355 

• Gold target thickness of 100 µm. 

• Vacuum beam pipe made of carbon fiber with a wall thickness of 0.5 mm. 

• Seven silicon tracking stations placed at the distances of 5, 10, 20, 40, 60, 80 and 100 cm downstream 

from the target. The first two tracking stations are 100 µm thick and the others are 200 µm 

thick. 

Figure 16.1 presents the simulated e + e − invariant mass spectrum resulting from 10 8 Au + Au central 

collisions at 25A GeV after the four cuts described in sec. 1.3 have been applied (black dashed line). 

The combinatorial background, obtained by the so called same-event technique (solid red line) is clearly 

dominating the spectrum. The background-subtracted spectrum, shown by the histogram, satisfactorily 

reproduces the signal input to the simulation as indicated by the blue dashed-dotted line, thus confirming 

the background subtraction procedure. From this analysis, we derive a signal-to-background ratios (S/B) 

of 95 and 250 for (ρ + ω) and φ mesons, respectively. 

] 

-1 

[(25 MeV) 

-4 

10 

- 

e 

+ 

e 

/dM 

- 

10 

e 

+ 

e 

dN 

-5 

-6 

10 

0 0.2 0.4 0.6 0.8 1 1.2 2 

[Gev/c ] 

M + - 

e e 

+ - e (1) 

+ 

e 

- 

e-) 

2* (e e+ 

)(e (2) 

polynomial fit to (2) 

(1) - polynomial fit 

Pluto + Geant4 

Figure 16.1: Simulated e + e − invariant mass spectrum for ideal case. 

In order to perform more realistic studies the pion misidentification and limited tracking efficiency were 

taken into account. Figure 16.2 presents invariant mass spectrum with 90% of tracking reconstruction 

efficiency. This effect lowers the signal-to-background ratio to 21 and 70 for (ρ + ω) and φ mesons, 

respectively. Another effect, the pion misidentification at the level of 0.01% lowers the S/B ratios to 

the values 60 and 148 for (ρ + ω) and φ mesons, respectively (see Fig. 16.3). The combined effect, 

inefficiency and pion misidentification, leads to S/B ratios equal to 14 and 37 for (ρ + ω) and φ mesons, 

respectively, as presented at figure 16.4.


] 

-1 

[(25 MeV) 

- 

e 

+ 

e 

/dM 

+ - 

e e 

dN 

-4 

10 

10 

-5 

-6 

10 

-7 

+ - e e (1) 

+ + - - 

2* (e e )(e e ) (2) 




10 

0 0.2 0.4 0.6 0.8 1 1.2 

2 

M + - [Gev/c ] 

Figure 16.2: Simulated e + e − invariant mass spectrum with 90% efficiency assumed. 

] 

-1 

[(25 MeV) 

- 

e 

+ 

e 

/dM 

+ - 

e e 

dN 

-4 

10 

10 

-5 

-6 

10 

-7 

10 

0 0.2 0.4 0.6 0.8 1 1.2 

2 

M + - [Gev/c ] 

e e 

+ - e e (1) 

+ + - - 

2* (e e )(e e ) (2) 




Figure 16.3: Simulated e + e − invariant mass spectrum with pion misidentification at the level of 0.01%. 

] 

-1 

[(25 MeV) 

- 

e 

+ 

e 

/dM 

- 

+ 

e e 

dN 

10 

10 

-4 

-5 

-6 

10 

0 0.2 0.4 0.6 0.8 1 1.2 

2 

M + - [Gev/c ] 

e e 

+ - e e (1) 

+ + - - 

2* (e e )(e e ) (2) 




Figure 16.4: Simulated e + e − invariant mass spectrum with 90% efficiency assumed and pion misidentification 

effect at the level of 0.01 % 

e e

17 Charmonium 

The measurement of charmonium is one of the key goals of the CBM experiment. The main difficulty 

lies in the extremely low multiplicity expected in Au+Au collisions near the J/ψ production threshold. 

CBM will detect the J/ψ meson in its leptonic decay channels. For the dielectron measurement, the main 

task is the separation of electrons and pions and the suppression of electrons originating from secondary 

interactions. In case of the measurement in the µ + µ − channel, it is mandatory to suppress muons from 

weak decays of pions and kaons. 

Figure 17.1 depicts the yields of open and hidden charm as function of beam energy, obtained by the 

Hadron String Dynamics (HSD) model. The calculations were performed for central events. These 

multiplicities serve as input for the simulations to be described in the next sections. The UrQMD and 

HSD predictions for the multiplicites of all relevant particles produced in central Au+Au collisions at 

different beam energies are given in tables 12.1 and tables 12.2 (chapter 12). 

Figure 17.1: Multiplicities of D 0 , D 0 , D + , D − and J/ψ predicted for central Au+Au collisions as functions of 

beam energy by the HSD model 

17.1 J/ψ detection via e + e − decay 

17.1.1 Signal and background simulation 

The simulation of signal and background were performed with the CBM software framework CBM- 

ROOT, using the standard setup with a gold target of 250 µm thickness, the beam pipe, the STS, the 

RICH and three TRD stations. 

For the J/ψ signal, we used the multiplicity as predicted by HSD. The J/ψ phase space distribution and 

the decay kinematics were calculated with the PLUTO event generator, assuming a thermal source with 

a temperature of 150 (170, 190) MeV for beam energies of 15 (25, 35) AGeV, respectively. 

357

358 Charmonium 

The main background sources are 

• e + e − pairs from conversion of γ (mostly from π 0 and η decay) in the material of target and detectors, 

• Dalitz decays of π 0 and η, 

• charged pions misidentified as electrons, 

• dielectronic decays of low-mass vector mesons, 

• semileptonic decay of open charm. 

The contribution from Drell-Yan is not yet considered. The background was obtained by transporting 

UrQMD events through the detector setup. Since this generator does not describe charmed hadrons and 

leptonic decays of vector mesons, these source were simulated by HSD and added to the UrQMD events. 

Because of the fact that track reconstruction and particle identification are not yet available, the following 

assumptions were made for the analysis: 

• The combined electron identification in RICH and TRD gives an electron efficiency of 90 %. A 

pion suppression factor of 10 −4 can be achieved. 

• The momentum resolution is 1 %. 

• Secondary particles created by interactions in the detectors can be recognised by the STS tracking 

routines. We therefore consider only particles originating from the target region. 

) 

-1 

(GeV 

t 

dn / dp 

10 

10 

1 

-1 

-2 

10 

-3 

10 

-4 

10 

-5 

10 

-6 

10 

-7 

10 

-8 

10 

π0 γ 

η 

± 

misdentified π 

D 

J/ Ψ 

-9 

10 

0 0.5 1 1.5 2 2.5 3 3.5 4 

pt 

(GeV/c) 

Figure 17.2: Transverse momentum distribution of electrons and positrons from various sources as calculated 

with UrQMD or HSD for central Au+Au collisions at 25 AGeV beam energy. Black: γ conversion in the target; 

blue: π 0 Dalitz decay; green: η Dalitz decay; magenta: misidentified charged pions for a suppression factor of 

10 −4 ; grey: open charm; red: J/ψ calculated with PLUTO assuming T = 170 MeV 

Figure 17.2 shows the transverse momentum spectra of accepted electrons for the different background 

sources, compared to the signal distribution. Most of the background processes create electrons with low

17.1. J/ψ detection via e + e − decay 359 

pt, in contrast to the leptonic J/ψ decay. A pt cut at 1 GeV/c already suppresses most of the background 

tracks without cutting seriously into the signal. 

17.1.2 Acceptance and efficiency 

The single electron acceptance is defined by requiring a hit in all three TRD stations. Since STS, RICH 

and TRD cover approximately the same angular acceptance, this implies that such tracks can be reconstructed 

in the STS. Figure 17.3 shows the pt and y distributions of the accepted J/ψ compared to the 

intial ones. The two-dimensional distribution of J/ψ accepted after the pt cut is shown in figure 17.4. 

Obviously, the pt cut does not restrict the acceptance region, which covers roughly one unit of rapidity. 

The total J/ψ efficiency, including geometrical acceptance and pt cut, is about 35 %. 

) 

-1 

dn/dp_t (GeV 

0.03 

0.025 

0.02 

0.015 

0.01 

0.005 

0 

0 0.5 1 1.5 2 2.5 3 3.5 4 

pt 

(GeV/c) 

dn/dy 

0.07 

0.06 

0.05 

0.04 

0.03 

0.02 

0.01 

0 

0 0.5 1 1.5 2 2.5 3 3.5 4 

y 

Figure 17.3: Distribution in transverse momentum (left) and rapidity (right) for J/ψ mesons. Black: all J/ψ 

decaying into e + e − ; red J/ψ accepted in the detector; blue: J/ψ accepted after the pt cut at 1 GeV/c on the decay 

daughters 

(GeV/c) 

t 

p 

2.5 

2 

1.5 

1 

0.5 

0 

1 1.2 1.4 1.6 1.8 2 2.2 2.4 2.6 2.8 3 

Figure 17.4: Yield of J/ψ mesons in the pt vs. rapidity plane for geometrically accepted pairs after the pt cut 

Y 

300 

250 

200 

150 

100 

50 

0


17.1.3 Signal to background ratio 

The invariant-mass spectrum is calculated from combinations of electrons and positrons (including 

misidentified pions) from the target region after applying the cut on pt. To obtain sufficient statistics 

from the simulated 145 k events from UrQMD, the event mixing technique was applied. The resulting 

“superevent” is equivalent to 2 · 10 10 events. Of course, by mixing events all physical correlations 

between the background electrons and positrons are neglected. Figure 17.5 compares the result of the 

event mixing technique with the invariant-mass spectrum obtained from the event-by-event analysis for 

a pt cut at 0.5 GeV/c. 

) 

2 

pairs/(20 MeV/c 

10 

10 

-3 

-4 

10 

-5 

-6 

10 

-7 

10 

-8 

10 

0 0.5 1 1.5 2 2.5 3 3.5 4 

2 

(GeV/c ) 

Figure 17.5: Invariant e + e − mass spectra calculated for background pairs with the superevent method (red 

histogram) and with the event-by-event method (blue histogram) for a transverse momentum cut of pt > 0.5 GeV/c 

The invariant-mass distributions for the pt cut of 1 GeV/c and for different assumptions about the pion 

suppression factor are shown in figure 17.6 (left). While at a pion suppression of 100 no J/ψ signal can 

be seen, a suppression of 10 −4 suffices to almost eliminate the influence of misidentified pions on the 

signal-to-background ratio. 

The signal-to-background ratio can be further improved by requiring a larger minimal transverse momentum 

for the single tracks. Invariant-mass spectra for different cut values are compared in figure 17.6. The 

J/ψ region of the spectra are shown in linear representation in figure 17.7. The signal yield is determined 

by fitting a Gaussian peak on top of a polynomial representing the combinatorial background. For the 

calculation of the signal-to-background ratio, the distributions are integrated in a ±2σm window around 

the peak. The resulting values of S/B and the J/ψ efficincy are given in table 17.1 for three different 

beam energies and pt cut values. 

pt cut S/B ε(J/ψ) S/B ε(J/ψ) S/B ε(J/ψ) 

GeV/c 15 AGeV 15 AGeV 25 AGeV 25 AGeV 35 AGeV 35 AGeV 

1 0.77 0.25 1 0.34 1.2 0.39 

1.3 1.48 0.13 2.5 0.17 3.5 0.19 

1.5 3.36 0.027 3.36 0.04 3.36 0.045 

Table 17.1: Signal-to-background ratio and J/ψ efficiency for central Au+Au collisions at 15, 25, and 35 AGeV. 

The efficiency comprises geometrical acceptance, losses due to the pt cut and electron identification efficiency. 

minv


2 

pairs/ 40 MeV/c 

5 

10 

4 

10 

3 

10 

0 0.5 1 1.5 2 2.5 3 3.5 4 

2 

( GeV/c ) 

minv 

2 

pairs / 40 MeV/c 

4 

10 

3 

10 

2 

10 

10 

pt 

>1GeV/c 

p > 1.3 GeV/c 

t 

p >1.5GeV/c 

t 

0 0.5 1 1.5 2 2.5 3 3.5 4 

2 

( GeV/c ) 

Figure 17.6: Invariant e + e − mass spectra representing 2 · 10 10 central Au+Au collisions at 25 AGeV. Left: for 

a transverse momentum cut of pt > 1GeV/c and different pion suppression factors (magenta: 10 −2 ; green: 10 −3 ; 

blue: 10 −4 ; red: without pion contamination). Right: For different transverse momentum cuts (magenta: pt > 1 

GeV/c; green: pt > 1.3 GeV/c; blue: pt > 1.5 GeV/c) and a pion suppression of 10 −4 . 

8000 

6000 

4000 

2000 

0 

2.6 2.8 3 3.2 3.4 3.6 

2 

minv 

(GeV/c ) 

2200 

2000 

1800 

1600 

1400 

1200 

1000 

800 

600 

400 

200 

0 

2.6 2.8 3 3.2 

minv 

3.4 

(GeV/c 

2 

) 

minv 

2.6 2.7 2.8 2.9 3 3.1 3.2 3.3 3.4 3.5 

2 

(GeV/c ) 

Figure 17.7: Invariant e + e − mass spectra for different pt cuts with fit in the J/ψ mass region for 2 · 10 10 central 

Au-Au collisions at 25 AGeV 

17.1.4 Online event selection 

The previous sections have shown that 10 10 or more events are required for the measurement of J/ψ 

in the dielectron channel. This corresponds to a beam time of the order of some hours, the envisaged 

interaction rate being 10 6 for central reactions. However, the limited event archival rate calls for a highly 

selective online event selection. The selection decision must be fast and efficient and should thus be 

based on a minimal amount of input data, coming from as few as possible detector subsystems. In this 

first approach, we have focussed on the information available from the Transition Radiation Detectors 

(TRD), which deliver tracking as well as electron ID information while having a sufficiently fast detector 

response. 

For the estimation of the event rejection possibilities using only TRD information, minimum bias Au+Au 

events at 25 AGeV beam energy generated by UrQMD were transported through the detector setup using 

GEANT3 as transport engine. Only charged pions and protons as the main contributors to the background 

were considered; the acceptance is defined by the first TRD station. We quantify the pion rejection due 

to the TRD measurement by the pion rejection factor PRF = 100 − k, where k is the percentage of 

500 

400 

300 

200 

100 

minv


misidentified pions. 

The main cut variable is the single track transverse momentum as motivated in the previous section. 

We assume in this first step perfect momentum determination. In a next step, the momentum will be 

reconstructed from the TRD information alone assuming that the particles originate from the target. A 

first approach to this problem is presented in the next section; alternatively, the momentum could be 

obtained quite performantly with the use of a lookup table. 

The event selection criterion is a pair of oppositely charged particles with an invariant mass greater than 

2 GeV/c 2 . Figure 17.8 shows the percentage of events fulfilling this criterion as a function of the single 

particle pt cut for different assumptions about the PRF. For a PRF of 98, equivalent to a pion suppression 

by a factor of 50 which is foreseen to be reached with the TRD detectors, the event rejection factor is 

about 15 for a pt cut at 1 GeV/c. This parametric study shows that the inclusion of information from 

ECAL or RICH, resulting in a better pion suppression, is probably needed to arrive at higher event 

rejection factors. 

Survivors [%] 

2 

10 

10 

1 

-1 

10 

PRF = 98 

PRF = 10 

0.4 0.6 0.8 1 1.2 1.4 

Transversal momentum Pt [GeV/c] 

Figure 17.8: Percentage of minimum bias Au+Au events (25 AGeV) that survived the event selection (a pair of 

oppositely charged particles with invariant mass greater than 2 GeV/c 2 ) as a function of the minimal single particle 

pt for different values of the PRF. The full lines show the simulation results for primary particles only, the dotted 

lines take in addition into account secondary particles produced by decays or interactions in the detector materials. 

17.1.5 Fast track reconstruction in the TRD 

To study the possibility of fast J/ψ reconstruction using only information from TRD, J/ψ decays into 

e + e − were simulated with the PLUTO generator and added to background UrQMD events (central 

Au+Au at 25 AGeV). The combined events were transported through the experimental setup in the presence 

of the inhomogeneous CBM magnetic field using the CBMROOT detector simulation framework. 

The TRD geometry consists of three stations located at 4 m, 6 m and 8 m distance from the target, respectively. 

Each station consists of three sensitive layers. The position resolution in x direction was assumed 

to be 0.4 mm, 0.5 mm and 0.6 mm for the three stations, respectively, and varied from 2.7 mm to 33 mm 

in the y direction according to the distance from the beam. The x and y directions alternate from layer to 

layer. This assumption emulates a MWPC type readout of the TRDs. 

A track is accepted if it traverses all three TRD stations. In this first approach, ideal track finding is 

assumed. The track parameters x, y, tx = px/pz and ty = py/pz, defined at the z position of the first TRD


layer, were obtained at by the Kalman filter technique (section 13.2.1). Since the TRDs are operated 

outside of the magnetic field, a simple track model can be used, resulting in a good timing performance 

of the Kalman filter algorithm. 

For the fast reconstruction of the original track parameters, we assume that the track originates from the 

primary vertex. Then, the inverse momentum can be approximated by 

q 

p = 

[x − (z − ztarget)tx] 1 +t 2 x 

 

1 +t 2 x +t 2 

y dl By 

(zmagnet − ztarget) 

, (17.1) 

where ztarget and zmagnet are the z coordinates of the target midplane and the centre of the magnet, respectively, 

and q the charge of the particle. The average field integral dl By is obtained by a Monte 

Carlo simulation of electrons from J/ψ decay as shown in figure 17.9. The relative precision is about 

5 %. Figure 17.10 demonstrates the accuracy of the momentum determination by this approximation. 

The average momentum resolution is about 8 %. 

Figure 17.9: Distribution of the magnetic field constant 

(zmagnet − zvertex) dl By for the simulated e + and 

e − from J/ψ decays 

Figure 17.10: Distribution of relative momentum 

residuals for e + and e − using the fast momentum reconstruction 

in the TRD (see text) 

Since in first approximation, the Bx and Bz components of the CBM dipole field are vanishing, the slope 

ty,i at the track origin can be approximated by ty, f measured in the TRD 1 . In the bending plane, tx, f 

will differ from tx,i by the deflection angle Δθ. Figure 17.11 shows the correlation between initial and 

final slope for pz = 1.3−1.4 GeV/c as resulting from the simulation. As expected, a linear dependence is 

obtained. By a fit, Δθ = tx, f −tx,i is derived. In this way, the deflection angle is calculated for 0.5GeV/c < 

pz < 1.5GeV/c. As can be seen in figure 17.12, its dependence on pz can be parametrised as Δθ = 

0.304GeV/(cpz). 

Using this empirical dependence, tx,i can be calculated from the measured tx, f . Figure17.13 compares its 

reconstructed to the true value. The original track slopes are reproduced with an accuracy of about 5 %. 

tx,i completes the momentum reconstruction at the primary vertex. 

The e + e − invariant mass spectrum, calculated with the momenta estimated by this method, is compared 

in figure 17.14 to the MC truth. Due to the limited precision of the momentum reconstruction, some 

signal pairs are shifted out of the J/ψ invariant mass region. Most of them (92 %), however, still satisfy 

a possible online condition minv > 2 GeV/c 2 (see section 17.1.4), as suggested in the previous section. 

1 The subscripts i and f stand for initial and final, respectively.


Figure 17.11: Distribution in the tx,i −tx, f plane for e + 

and e − from simulated J/ψ decays. The momentum range 

is 1.3 - 1.4 GeV/c. 

Figure 17.12: Deflection angle as function of momentum 

Figure 17.13: Reconstructed versus true tx,i for e + and e − from J/ψ decay 

Figure 17.14: Invariant e + e − mass spectrum for tracks accepted in the TRD system. Left: calculated using the 

true electron momenta; right: calculated with the momenta obtained by the fast TRD reconstruction (see text). The 

relative normalisation of signal and background spectra is arbitrary. Note that the background spectrum does not 

contain misidentified charged pions.

17.2. J/ψ detection via µ + µ − decay 365 

17.1.6 Conclusions and next steps 

The feasibility studies presented in the previous sections have demonstrated that the measurement of 

J/ψ should be feasible in the foreseen CBM detector setup provided that a charged pion suppression of 

10 3 can be reached. The required event number is of the order of 10 10 for 25 AGeV beam energy; it 

is a strong function of beam energy due to the steep J/ψ excitation function near the threshold. The 

event rejection factors achievable with the use of TRD only are of the order of 10 - 20 depending on the 

transverse momentum cut applied and the pion suppression achieved by the TRD. 

The next steps towards more realistic simulations are: 

• implementation of track and momentum reconstruction with the STS based on realistic digitisers, 

in particular for the Silicon Strip detectors, 

• full tracking from target to ECAL based on digitizers for STS, TRD, RPC and ECAL, 

• electron identification and pion suppression obtained from realistic simulations of RICH and TRD, 

• further development of fast momentum reconstruction methods using only TRD information, 

• inclusion of ECAL and/or RICH information for the fast event selection. 

It is clear that these studies will provide crucial feedback for detector optimisations. One case in point 

is the TRD, for which the position resolution requirements will be mainly derived based on J/ψ selection 

requirements. The detector layout (distance between layers and stations) and material budget are 

important issues, too. 

17.2 J/ψ detection via µ + µ − decay 

In addition to the dielectron measurement, the possibilitiy of the detection of J/ψ mesons via their decay 

into µ + µ − has been studied. This measurement would require a muon detector located after the TOF 

wall. It might be either a longitudinally segemented ECAL or an additonal dedicated device behind the 

ECAL. The study presented in this section has been started recently and is still in a preliminary state. 

17.2.1 Signal and background simulation 

The simulation setup is the same as used for the dielectron channel (section 17.1). The J/ψ signal decay 

was simulated with the PLUTO generator assuming a thermal source with a temperature of 130 MeV. 

The J/ψ multiplicity was taken from the HSD prediction (see figure 17.1). The background, consisting 

mainly of weak decays of charged pions and kaons, was calculated with the UrQMD event generator. 

Both signal and background are transported through the detector setup with CBMROOT employing 

GEANT3. All simulations were done for central Au+Au collisions at 25 AGeV beam energy. 

In this first step, the analysis is based on the following simplifying assumptions: 

• The muons are detected with 100% efficiency in a plane located 11 m downstream of the target. 

• Ideal track finding is assumed. The momentum resolution was assumed to be 1%. 

Fig. 17.15 depicts the transverse momentum distributions of muons at a distance of 11 m downstream 

of the target. At this distance about 7% of the pions and about 26% of the kaons underwent a weak


t 

dn/dp 

10 

10 

-1 

-2 

-3 

10 

-4 

10 

-5 

10 

-6 

10 

-7 

10 

-8 

10 

-9 

10 

-10 

10 

-11 

10 

Mother particles 

π+/ 

π- 

K+/K- 

K0 long 

1% misidentified π+/ 

π- 

J/ Ψ 

0 0.5 1 1.5 2 2.5 3 3.5 4 

pt 

(GeV/c) 

Figure 17.15: Transverse momentum distribution of background and signal muons for central Au+Au collisions 

at 25 AGeV 

decay into a muon and a muon neutrino. For muon transverse momenta above 1 GeV/c, pions and kaons 

contribute almost equally to the background. In addition to these background sources, figure 17.15 

contains a contribution of 1% misidentified pions and, for comparison, muons from the decay of J/ψ 

mesons. 

Figure 17.16 compares the two-dimensional distribution of the muon transverse momenta for signal and 

background pairs (the relative yields do not reflect realistic multiplicities). The plot illustrates that a cut 

on the single muon transverse momentum of pt > 1 GeV/c suppresses about 99% of the background but 

only about 20% of the signal. 

(GeV/c) 

- 

μ 

t 

p 

4 

3.5 

3 

2.5 

2 

1.5 

1 

0.5 

0 

0 0.5 1 1.5 2 2.5 3 3.5 4 

pt 

μ+ 

(GeV/c) 

45 

40 

35 

30 

25 

20 

15 

10 

5 

(GeV/c) 

- 

μ 

t 

p 

4 

3.5 

3 

2.5 

2 

1.5 

1 

0.5 

0 

0 0.5 1 1.5 2 2.5 3 3.5 4 

pt 

μ+ 

(GeV/c) 

Figure 17.16: Transverse momentum of µ − versus transverse momentum of µ + for muons from J/ψ decay (left) 

and for background muons (right) 

17.2.2 Signal to background ratio 

To obtain the combinatorial background in the dimuon invariant mass spectrum after the pt cut with 

sufficient statistics, the superevent method (combination of muons from different events) was used as 

described in section 17.1. The spectrum calculated from the 21,000 simulated events thus corresponds 

to 4.4 · 10 8 central Au+Au events. Figure 17.17 demonstrates the validity of this method by comparing 

4 

10 

3 

10 

2 

10 

10


the spectra calculated event by event and from the superevent for a pt cut at 0.5 GeV/c. 

counts/(event*10MeV) 

-2 

10 

-3 

10 

-4 

10 

-5 

10 

10 

-6 

-7 

10 

-8 

10 

-9 

10 

Superevent 

Pt > 0.5 GeV/c 

Pt > 1 GeV/c 

Event-by-Event 


Pt > 1 GeV/c 

0 0.5 1 1.5 2 2.5 3 3.5 4 

m inv 

(GeV/c 

Figure 17.17: Dimuon invariant mass distributions of the background dimuon pairs. Red and black histogram: 

superevent method. Blue and green histogram: event-by-event method. The red and blue histograms correspond 

to a transverse momentum cut of pt > 0.5 GeV/c, the green and black histograms to pt > 1.0 GeV/c. The spectra 

are normalised to on event (central Au+Au at 25 AGeV). 


-2 

10 

-3 

10 

-4 

10 

-5 

10 

10 

-6 

-7 

10 

-8 

10 

-9 

10 





0 0.5 1 1.5 2 2.5 3 3.5 4 

m inv 

(GeV/c 

Figure 17.18: Dimuon invariant mass spectra for central Au+Au collisions at 25 AGeV, including a 1% contamination 

of misidentified π + /π − mesons. The colors refer to different transverse momentum cuts (red: pt > 0.5 

GeV/c; green: pt > 1 GeV/c; blue: pt > 1.3 GeV/c; magenta: pt > 1.5 GeV/c). 

According to HSD calculations, the J/ψ multiplicity in central Au+Au collisions at 25 AGeV is about 

1.9 × 10 −5 . Taking into account the branching ratio of 6%, the yield of J/ψ mesons decaying into a 

dimuon pair is only about 10 −6 . Figure 17.18 presents the dimuon invariant mass spectra for different 

values of the transverse momentum cut on the single muons. The spectra contain a contribution of 1% 

misidentified charged pions. The J/ψ signal is not visible for any choice of the minimal pt. Figure 

17.19 shows the efficiency for the J/ψ detection and the signal to background ratio, calculated in a ±2σm 

window around the signal peak, as function of the minimal single muon pt. For a cut at 1 GeV/c, the S/B 

2 

) 

2 

)


ratio is about 0.2 %, the efficiency being about 42 %. Apparently, pt cuts above 1.5 GeV/c are prohibitive 

because of the rapid drop in detection efficiency. 

The influence of charged pion misidentification is illustrated in figure 17.20, showing the invariant mass 

spectrum for different charged pion suppression factors. Varying the suppression between 10 −2 and 10 −3 

has no major impact on the background distribution in the J/ψ mass region. 

efficiency (%) 

2 

10 mu+/mu- from J/Psi 

10 

0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 

p cut (GeV/c) 

t 

signal/background 

-1 

10 

-2 

10 

-3 

10 

0.4 0.6 0.8 1 1.2 1.4 1.6 

p cut (GeV/c) 

t 

mu+/mu- 

Figure 17.19: Efficiency for J/ψ meson detection (left panel) and signal to background ratio (right panel) as 

function of the transverse momentum cut pt 


-5 

10 

-6 

10 

-7 

10 

Pt > 1 GeV/c. Misidentified pions: 

1.0 % 

0.5 % 

0.1 % 

0 0.5 1 1.5 2 2.5 3 3.5 

m inv 

(GeV/c 

Figure 17.20: Invariant mass spectrum of muon pairs from central Au+Au collisions at 25 AGeV after a transverse 

momentum cut of pt > 1 GeV/c. The spectra include the combinatorial background, the J/ψ signal and 

different contributions of misidentified π + and π − mesons (red: 1%, green: 0.5%, blue: 0.1%). 

Further suppression of the background muons from weak meson decays could achieved by measuring the 

kink angle between the trajectory of the meson and its decay daughter. The distributions of this angle are 

shown in figure 17.21. By a cut in this variable, most of the muon background originating from pion or 

kaon decay can be eliminated as shown in figure 17.22. The remaining background is almost exclusively 

due to misidentified pions, which cannot be suppressed by the kink angle cut. A measurement of J/ψ in 

2 

)


the dimuon channel thus requires a charged pion supression of 10 −3 or more and a measurement of the 

kink angle with a precision of a degree or better. 

counts/event 

10 

1 

-1 

10 

-2 

10 

-3 

10 

-4 

10 

11 m 

all 

pt > 1 GeV/c 

0 20 40 60 80 100 120 140 160 180 

kink angle, degrees 

counts/event 

10 11 m 

all 

pt > 1 GeV/c 

1 

-1 

10 

-2 

10 

-3 

10 

-4 

10 

0 20 40 60 80 100 120 140 160 180 

kink angle, degrees 

Figure 17.21: Kink-angles between the trajectories of mesons and their decay muons with transverse momentum 

cut pt > 1 GeV/c (green) and without (red). Left panel: pions; Right panel:kaons. 


-5 

10 

-6 

10 

-7 

10 

-8 

10 

-9 

10 

Pt > 1 GeV/c, 0.1 % misidentified pions 

all 

kink angle < 1.0 degree 



0 0.5 1 1.5 2 2.5 3 3.5 4 

m inv 

(GeV/c 

Figure 17.22: Invariant mass spectrum of muon pairs from central Au+Au collisions at 25 AGeV. The spectra 

include the combinatorial background, the J/ψ signal and a 0.1% contribution of misidentified π + and π − mesons. 

The background is suppressed by the transverse momentum cut of pt > 1 GeV/c, and by different kink angle cuts 

of 1.0 o (red histogram), 0.5 o (blue histogram), and 0.1 o (green histogram). 

17.2.3 Identification of primary muons 

As shown in the previous section, a key issue in the measurement of J/ψ in the dimuon channel is the 

suppression of muons from weak decays of pions and kaons. The separation of these muons from those 

emerging from the target area can be accomplished with a number of tracking devices placed on the way 

to the muon-identification device. In each tracking station of such a system, the position and the flight 

2 

)


direction of a charged particle will be registered. In the current CBM setup, the STS and the three TRD 

stations serve as such tracking tools. 

A decay of a pion or a kaon into a muon and a neutrino can be detected due to a kink in the trajectory. 

In the following only pions decays are considered because kink angles for kaon decays are several times 

larger. In case the decay occurs between two consecutive tracking stations, the position of the track of 

the decay-muon extrapolated from the second tracking plane to the first tracking plane will substantially 

differ from the position of the pion registered in the first plane. If a pion does not decay between two 

consecutive planes, the accuracy of the extrapolation will be limited by the multiple scattering in between 

the two planes, which is practically the same for pions and muons. 

2*R_max 

muon 

pion 

First Detector 

Fit 

Point 

Multiple Scattering 

Kink 

Second Detector 

muon 

muon 

ACCEPTED 

REJECTED 

a.u. 

6 

5 

4 

3 

2 

1 

muons multiple scattering 

RMS = 0.084 

pions kink 

RMS = 0.898 

0 

-2 -1.5 -1 -0.5 0 0.5 1 1.5 2 

distance in X coordinate [cm] 

Figure 17.23: Extrapolation of tracks between two consecutive tracking planes. The track in the second plane 

is extrapolated back to the first plane placed 2 m upstream. The distance to the original point in the first plane is 

projected onto an axis perpendicular to the initial momentum of a particle. Magenta: muons that undergo smallangle 

scattering, blue: pions that decay into muons and neutrinos. Small-angle scattering takes place only in the 

air in between the two planes. A perfect position resolution in both tracking planes is assumed. 

The impact of pion decay between two neighbouring tracking stations was studied in a first step assuming 

ideal position resolution and accurate determination of the track angle in the tracking stations. Only 

multiple scattering in the air between the stations has been taken into account. Figure 17.23 shows results 

of simulations for pions decayed in between the two planes (blue histogram) and muons (magenta). The 

distances between the true positions registered in the first tracking plane and positions extrapolated from 

the second plane were projected onto one of the axes perpendicular to the original direction of particles. 

The momenta of muons and pions at the first tracking plane are 2 GeV/c; the distance between the two 

planes is 2 m. 

In order to study the efficiency of the rejection of decay-muons, we define a radius Rmax around the true 

position in the first tracking plane in which the extrapolated point will be searched for. All cases for 

which the extrapolation falls outside Rmax are rejected as decay muons. Reducing Rmax results in a better 

rejection of pion decays but also in a smaller efficiency for true muons. We define the Rejection Factor 

RF as 

f romπ 

Nµ (R < Rmax) 

RF = 

Nall . (17.2) 

π 

It quantifies the amount of background due to pion decays that survive the selection criterion. The left 

panel of figure 17.24 shows RF as a function of the distance between the two tracking planes for different 

values of Rmax and for pions with 2 GeV/c momentum. The dependence of RF on the pion momentum 

for a fixed Rmax = 1 mm is illustrated in the right panel of figure 17.24.


RF 

× 

2.2 

2.0 

1.8 

1.6 

1.4 

1.2 

1.0 

0.8 

0.6 

0.4 

0.2 

-3 

10 

0.5mm 

1mm 

1.5mm 

2mm 

2.5mm 

3mm 

0.0 

50 100 150 200 250 300 350 400 450 500 

distance [cm] 

RF 

0.7 

0.6 

0.5 

0.4 

0.3 

0.2 

0.1 

-3 

× 10 

1 GeV 

2 GeV 

3 GeV 

4 GeV 

5 GeV 

6 GeV 

7 GeV 

8 GeV 

9 GeV 

10 GeV 

0.0 

50 100 150 200 250 300 350 400 450 500 

distance [cm] 

Figure 17.24: Left: rejection of pions with 2 GeV/c momentum for different selection criteria Rmax as a function 

of distance between two tracking planes. Right: rejection of pions with selection criterion Rmax = 1mm for different 

momenta as a function of distance between two tracking planes. 

The efficiency of the selection for true muons is defined as 

EF = Ntrue 

µ (R < Rmax) 

N true 

µ 

(17.3) 

and plotted in figure 17.25 as a function of the distance of the tracking stations for fixed muon momentum 

of 2 GeV/c and different Rmax (left panel) and for fixed Rmax = 1 mm and different muon momenta (right 

panel). For a given distance d between the tracking planes and for a given selection criterion Rmax, both 

RF and EF rise with particle momenta. However, the increase of the efficiency is stronger. Therefore, it 

is plausible to conclude that the capability of separation of true muons from decay muons improves with 

increasing particle momenta. 

EF 

1.0 

0.9 

0.8 

0.7 

0.6 

0.5 

0.4 

0.3 

0.2 

0.1 

0.5mm 

1mm 

1.5mm 

2mm 

2.5mm 

3mm 

0.0 

50 100 150 200 250 300 350 400 450 500 

distance [cm] 

EF 

1.0 

0.9 

0.8 

0.7 

0.6 

0.5 

0.4 

0.3 

0.2 

0.1 

1 GeV 

2 GeV 

3 GeV 

4 GeV 

5 GeV 

6 GeV 

7 GeV 

8 GeV 

9 GeV 

10 GeV 

0.0 

50 100 150 200 250 300 350 400 450 500 

distance [cm] 

Figure 17.25: Left: acceptance of true muons with 2 GeV/c momentum for different selection criteria Rmax as a 

function of distance between two tracking planes. Right: acceptance of true muons with selection criterion Rmax = 

1 mm for different momenta as a function of distance between two tracking planes. 

In order to study the signal to background ratio for the J/ψ identification, the following assumptions were 

made:


• the J/ψ multiplicity is 1.9 · 10 −5 per central event, 

• the phase-space distribution of J/ψ is according to the source parameters T=150 MeV and Δy = 0.8, 

• J/ψ decays into muons with a branching ratio of 5.97 %. 

For the background, 1000 central Au+Au events at a beam energy of 25 AGeV generated by UrQMD 

were used. The geometrical acceptance of the CBM setup was roughly taken into account by a cut in the 

laboratory polar angle 5 o < θlab < 30 o . Out of 700 pions produced on average per central event, 400 fall 

into the geometric acceptance. Moreover, on average 6 of them have transversal momenta larger than 1 

GeV/c, which, due to the geometrical cut on θLAB, corresponds almost perfectly to the lower momentum 

limit of 2 GeV/c. Choosing Rmax = 1 mm and the distance between the two tracking planes as 2 m, the 

rejection factor for pions of 2 GeV/c momenta is 5 · 10 −4 . Thus, one can expect that the mean number of 

background muon pairs, misidentified as true muons, will be less than 9 · 10 −6 per central event. 

a.u. 

1 

-1 

10 

0 0.5 1 1.5 2 2.5 3 3.5 4 

2 

[GeV/c ] 

Figure 17.26: Invariant mass spectra of muon pairs. Magenta: true pairs from J/ψ decays, blue: background 

due muons from pion decays. The relative normalisation of both histograms reflects the expected ratio of signal to 

background pairs. 

On the other hand, in one central event about 0.8 · 10 −6 true muon-pairs from J/ψ decays will be emitted 

on the average into the geometrical acceptance. Almost all of these muons have transverse momenta 

larger than 1 GeV/c, and about 65% of them pass through the selection criterion as defined above. Therefore, 

one expects about 4 · 10 −7 signal pairs per central event. The invariant mass spectra of background 

and signal muon pairs are shown in figure 17.26, where the integral of the signal distribution is 30 times 

smaller than that of the background. The signal peak is calculated assuming a momentum resolution of 

1%. In the scenario outlined above, one can expect a signal to background ratio (S/B) of about 7. 

17.2.4 Conclusions and next steps 

The study presented in the previous sections gives only a rough impression on the feasibility of the J/ψ 

measurement via the dimuon decay because of the idealizing assumptions made. In order to arrive at 

more realistic estimates one has to take into account: 

• the limited identification capability of the muon detector, 

• the material budget in the detectors increasing the effect of the small-angle scattering, 

• the finite spatial resolution of the tracking planes, 

minv


• the occupancy in the tracking planes limiting the global tracking close to the target area, 

• the contribution of kaon decays to the background, 

• the increase of the background due to secondaries. 

The study of these issues will be possible once tracking routines become available, which is foreseen 

for the first half of the year 2005. In addition, the possibility of employing a pion absorber after the 

Silicon Tracking System, eliminating most of the background caused by pion and kaon decay, will be 

investigated.

374 Charmonium

18 Open charm 

One of the major experimental challenges of the CBM experiment is the measurement of the D-meson 

hadronic decay in the environment of a heavy-ion collision. Due to the extremely low D multiplicity 

close to threshold, background reduction exploiting the D 0 displaced vertex topology is mandatory. The 

online event selection, required to reduce the envisaged reaction rate of 10 MHz down to the archival rate 

of 25 kHz, necessitates fast and efficient track reconstruction algorithms and high resolution secondary 

vertex determination. Particular difficulties in recognizing the displaced vertex of the rare D meson 

decays are caused by: 

• weak hyperon decays producing displaced vertices between the target and first silicon tracking 

station; 

• small angle scattering in detectors and beam pipe limiting the accuracy of track reconstruction and 

vertexing. 

In this chapter, strategies for background suppression in the open charm measurement will be discussed. 

We concentrate on the D 0 meson which decays into K − and π + with a branching ratio of 3.8%. The D 0 

mean life time is 127 µm/c. 

18.1 Signal and background simulation 

Signal D 0 decays were generated assuming a Gaussian distribution in rapidity (σy = 1) and a thermal 

spectrum in pt with T = 200 MeV. Figure 18.1 shows the pt-y and zvertex distributions of the generated 

open charm decays. 

The hadronic background for central Au+Au collisions at 25 AGeV was generated using the UrQMD 

model. Each signal decay was embedded into one background event. The combined tracks were transported 

through the CBM detector using the simulation framework CBMROOT (see section 10.7). The 

standard geometry of the STS subdetector (7 stations at z = 5, 10, 20 , 40, 60, 80 and 100 cm) was used. 

The thickness of the first two stations is 100 µm, that of the others is 200 µm. 

In this first step, the simulation has been performed without magnetic field. Ideal track finding was 

assumed. The momentum resolution was assumed to be 1 %. No particle identification was used. 

18.2 Tracking and Vertexing 

All tracks traversing at least four STS stations are taken into account for the analysis. The track parameters 

are obtained by use of the Kalman filter algorithm (section 13.2.1). The primary vertex was 

determined as described in section 13.3.2 from all tracks reconstructed in the STS excluding those which 

formed well detached vertices like K0 S and Λ decays. The accuracy achieved by the primary vertex 

reconstruction is of the order of several µm in beam direction. 

To reconstruct secondary vertices made of two or three tracks, the algorithm described in section 13.4.1 

has been used. Figure 18.2 shows the achieved resolution of 57 µm in the secondary vertex z coordinate. 

The reconstruction precision can be further improved by applying a mass constraint in the Kalman filter 

(see section 13.4.2). 

375

376 Open charm 

Figure 18.1: (Left) pt-y and (right) zvertex distributions of the generated D 0 decays into π + and K − . 

Figure 18.2: Difference of reconstructed and true z position of the D 0 → π + K − decay vertex. 

18.3 Background reduction 

To suppress the abundant combinatorial background in the invariant mass spectrum, the following cut 

variables have been studied: 

• IP: impact parameter of the track in the primary vertex z plane;

18.3. Background reduction 377 

• P: track momentum; 

• PT: track transverse momentum; 

• ZV: z position of track pair vertex; 

• C2: χ 2 of pair vertex fit; 

• PC: collinearity of pair momentum and pair vertex. 

All cuts are optimized by maximization of the significance S/ √ S + B, where S is the number of accepted 

signal pairs and B the number of background pairs, observed in the whole invariant mass (IM) region. 

Figures 18.3-18.5 demonstrate the optimization procedure for the IP, PT and ZV cuts. 

Figure 18.3: Left panel: single track impact parameter distributions for K − and π + from the D 0 decay (red line) 

and background tracks (black line). Right panel: significance S/ √ S + B as a function of the cut parameter value. 

The maximum is obtained at bmin = 80µm. 

Figure 18.4: Left panel: single track pt distributions for K − and π + from the D 0 decay (red line) and background 

tracks (black line). Right panel: significance S/ √ S + B as a function of the cut parameter value. The maximum is 

obtained at pt,min = 0.5 GeV/c. 

Due to the displaced decay vertex of the D 0 , the single track impact parameter cut and the cut on the 

pair vertex z coordinate are most efficient in reducing the background. In addition, the high q value of


Figure 18.5: Left panel: two-track vertex z distributions for K − and π + pairs from the D 0 decay (red line) and 

background pairs (black line). Right panel: significance S/ √ S + B as a function of the cut parameter value. The 

maximum is obtained at zv,min = 250µm. 

cut optimized value signal efficiency [%] 

track IP cuts 80 0.23 GeV/c reduced the combinatorial background by another factor of 

two. 

18.4 Analysis and results 

The combination of the cuts described in the previous section reduced the background by a factor 2 · 10 4 

in the signal mass region. The signal efficiency is about 11 %, the geometrical acceptance about 48 %.

18.4. Analysis and results 379 

Figure 18.6: Monte Carlo invariant mass spectra after applying various single particle cuts. Black: no cuts; 

green: P cut, blue: PT cut; orange: IP cut. The magenta line shows the spectrum obtained by assuming perfect 

PID. Light blue: all single particle cuts; red: all cuts including cuts on the pair. 

Figure 18.7: The Armenteros-Podolanski plot: transverse momentum ˜pt of the oppositely charged decay products 

versus their asymmetry in longitudinal momenta p ± L . Only pairs with a distance of closest approach less than 

0.5 mm are considered. 

In order to determine a signal to background ratio with such a large background suppression factor at least 

few times 10 7 MC events need to be simulated. This is a time consuming task and in order to estimate 

the shape of the background in the signal IM region the so-called "MC super event" technique [258] 

was used. The superevent, constructed out of 6,000 UrQMD events transported through the setup, is 

equivalent to 3.6 · 10 6 events. Using the shape of the invariant mass background, calculated from this 

superevent, and taking into account the D 0 + ¯D 0 multiplicity of about 1.5·10 −4 , the branching ratio, geo-


metrical acceptance and signal efficiency, we arrive at the invariant mass spectrum shown in figure 18.8, 

which is roughly equivalent to 30 min of data taking at 1 MHz interaction rate for central Au+Au collisions. 

The D 0 detection rate is about 1,000 per hour. It should be noted that this number is strongly 

dependent on the primary D 0 multiplicity. 

Figure 18.8: Invariant mass spectrum after all cuts expected for 30 min of data taking at 1 MHz interaction rate 

for central collisions. The red line shows a Gaussian fit to the signal on top of a linear background. 

18.5 Influence of the material budget 

One limiting factor of the open charm detection is the material budget in the tracking stations, degrading 

the secondary vertex detection capability through multiple scattering. The currently preferred solution 

is to use MAPS detectors which introduce a minimal amount of material. Alternative choices, like 

e.g. hybrid pixel sensors would be much more massive. To study the influence of the material budget, 

the simulation as described before has been performed for 700 µm thick tracking stations. The analysis 

was carried out in complete analogy to the standard setup. 

Figure 18.9 compares the z vertex and impact parameter distributions for the simulation with thick tracking 

stations to the results obtained with the standard setup. The increased influence of multiple scattering 

is clearly visible. In order to obtain a comparable background suppression, the cuts have to be chosen 

more restrictive, resulting in a decrease of the signal efficiency by a factor of about four. 

18.6 Next steps 

The next steps are: 

• usage of tracking in the inhomogeneous magnetic field in the STS for the D 0 vertex finding, including 

realistic digitized detector response; 

• analysis of the D + decaying to K − + π + + π + (and charge conjugate);

18.6. Next steps 381 

Figure 18.9: Reconstructed z-vertex of π + K − pairs (left) and single track impact parameters (right) calculated 

for background UrQMD events. The green histograms correspond to 700 µm thick tracking stations. 

• optimization of the STS geometry with respect to D 0 sensitivity; 

• study the beam energy dependence of the D 0 measurement feasibility.

382 Open charm

19 Event-by-event fluctuations 

In the vicinity of the critical point, fluctuations of chemical and kinetic observables from event to event 

are expected. We have investigated the ability of the CBM detector to detect fluctuations in the eventwise 

K/π and p/π ratio by usage of the TOF system. This requires to measure the number of pions, kaons 

and protons in single events. Due to the limited number of tracks in the detector acceptance (for central 

Au+Au at 25 AGeV around 360 pions, 25 kaons and 80 protons are accepted), fits to the squared mass 

distributions as described in chapter 14 will not be possible. 

We therefore use the probability density function defined in equation 14.3 and introduce for N measured 

hadrons in the event the functional 

N 

L({θα}) := PDF(pi,m 2 N 

i ;{θα}) = θαr α (pi) f α p (m 2 i ) (19.1) 

i=1 

where the index α denotes the particle species (pion, kaon, proton). The momentum spectra r α (p) and the 

squared mass distributions f α (p,m 2 α) are obtained from the inclusive analysis as described in chapter 14. 

The normalisations {θα} are free parameters. The functional is evaluated from the measured p and m 2 

of all tracks in the event. 

Maximising L with respect to {θα} gives the best estimate of the relative particle yields {θ ∗ α}, from 

which the eventwise particle ratios can be calculated. The maximisation procedure has to be performed 

for each event. Technically, it is more convenient to minimise the functional 

events 

4 

10 

3 

10 

2 

10 

10 

1 

l := −ln(L) = − 

data 

mixed events 

i=1 

N 

ln PDF(pi,m 2 i ;{θα}) 

i=1 

0 0.02 0.04 0.06 0.08 0.1 0.12 0.14 0.16 0.18 0.2 

/< π> 

events 

4 

10 

3 

10 

2 

10 

10 

1 

α 

data 

mixed events 

0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2 

+ /< π > 

(19.2) 

Figure 19.1: The event-by-event K/π (left) and p/π (right) ratios. The points show the distribution for real 

events, the histogram that for mixed events. 

We define as event-by-event fluctuation the RMS of the distribution of the fluctuating variable normalised 

to the mean value. It will have contributions from finite number statistics, experimental resolution and 

the "true" (dynamical) fluctuations: 

σ 2 = σ 2 stat + σ 2 exp + σ 2 dyn = σ 2 background + σ 2 signal 

(19.3) 

The background contribution (finite number statistics and detector resolution) can be assessed by event 

mixing. For this purpose, a pool of tracks from real events is created. For each real event, a mixed event 

383

384 Event-by-event fluctuations 

UrQMD detector acceptance + PID 

ratio data [%] mixed events [%] data [%] mixed events [%] 

/< π > 14.3 ± 0.05 14.1 ± 0.04 18.7 ± 0.06 18.6 ± 0.06 

/< π > 7.9 ± 0.02 9.7 ± 0.03 11.1 ± 0.04 12.6 ± 0.04 

Table 19.1: Relative widths of the event-by-event particles ratio distributions for data and mixed events 

with the same track multiplicity is created by randomly selecting tracks out of the pool. Each track of 

the pool is used only once; all tracks inside one mixed event originate from different real events. The 

analysis is then performed for real and for mixed events. From the widths of the event-by-event particle 

ratio distributions, the dynamical fluctuations are calculated by 

 

σdyn = + 

σdyn = − 

σ 2 data − σ2 mixed (σ 2 data > σ2 mixed ) 

 

σ2 mixed − σ2 data (σ 2 data < σ2mixed ) . 

ratio UrQMD detector acceptance + PID 

/< π > 2.4 ± 0.4 2.3 ± 0.7 

/< π > -5.6 ± 0.06 -5.9 ± 0.1 

Table 19.2: Dynamical event-by-event fluctuations of the particle ratios 

(19.4) 

This analysis has been performed on 50,000 central Au+Au collision at 25 AGeV beam momentum, 

simulated with UrQMD and transported through the CBM setup. The TOF resolution was assumed to be 

80 ps. For the distributions f α (p,m 2 ) a Gaussian shape was assumed as shown in chapter 14. Figure 19.1 

shows the distributions of the K/π and p/π ratios for both real and mixed events. The values of the 

relative width are listed in table 19.1. The resulting dynamical fluctuations can be found in table 19.2. It 

can be seen that the fluctuations originally present in the UrQMD events are satisfyingly reproduced by 

the method. 

> 

π 

) /< 

dyn 

σ 

( 

δ 

1 

0.9 

0.8 

0.7 

0.6 

0.5 

0.4 

2 4 6 8 10 12 14 

N , 1e4 ev 

> 

+ 

π 

) /< 

dyn 

σ 

( 

δ 

0.14 

0.12 

0.1 

0.08 

0.06 

2 4 6 8 10 12 14 

N , 1e4 ev 

Figure 19.2: Statistical error of the dynamical event-by-event fluctuations of the K/π (left) and the p/π ratio 

(right) as functions of the number of analysed events. The solid line shows the expected behaviour ∼ 1/ √ N. 

The accuracy of the measurement can be improved by increasing the event statistics. The statistical 

error on σdyn is shown in figure 19.2 as function of the number of events used for the analysis. It is 

approximately proportional to 1/ √ Nev. For 2·10 6 events, the error of the K/π fluctuations is of the order 

of 0.1%.

Part IV 

Infrastructure and Safety 

385

20 Cave 

The CBM cave hosts both the HADES detector and the CBM detector. The experimental hall has an 

area of 38×22 m 2 and requires a height of 20 m for the CBM detector and its handling with a crane. 

The cave can be entered via a variable labyrinth having a maximum opening of 6×6 m 2 which permits 

to transport large and heavy parts into the cave. Even larger parts are transported into the cave via an 

opening in the ceiling which has a size of 10×10 m 2 . The cave is equipped with a crane which can lift 

15 tons. The detectors require a beam line with a height of 2.7 m. A side view and a top view of the cave 

and the detectors is presented in the figures 20.1 and 20.2. The experimental hall requires air condition 

at a temperature of 20 - 25 o C. 

The entrance to the experimental hall (labyrinth) is integral part of a building which provides room for 

• detector and cave supplies, 

• mounting the detectors, 

• computer center, 

• operation of the experiment (control room) 

• offices. 

The CBM annex building covers an area of 700 m 2 distributed over 3 storeys. As the entrance to the cave 

is below ground level, the building contains a shaft with a size of 6×6 m 2 in all 3 storeys. The shaft is 

equipped with a 15 tons crane. 

387

388 Cave 

Figure 20.1: Side view of the CBM experimental hall equipped with the HADES detector and with the CBM 


Figure 20.2: Top view of the CBM experimental hall.

21 Radiation protection 

According to the radiation protection regulations in Germany the dose rate outside the cave must not 

exceed a value of 5 · 10 −7 Sv/h. Typically, the interaction probability of the primary beam with the 

target is about 1 %. Therefore, nearly 100 % of the beam has to be stopped in a dump located behind 

the experimental setup. The concrete shielding of the cave and the geometry and the dimensions of the 

Iron beam dump haven been studied with the Monte-Carlo-code FLUKA [260, 261]. As FLUKA is not 

capable of transporting heavy-ions, the primary beam was assumed to be composed of protons. The 

calculated dose values were finally scaled with the mass number of gold in order to have a conservative 

estimate for a gold beam. The expected dose rates in the CBM experimental hall for 10 9 Au ions per 

second at an energy of 30 AGeV are illustrated in figure 21.1. The shielding provided by the cave walls 

is sufficient to reduce the dose rate outside the cave to a value of 1 · 10 −7 Sv/h (blue color). 

Länge / m 

40 

30 

20 

10 

0 

Schnitt an Höhe=2m 

Strahleintritt 

Target 

Fe, 3mm 

Labyrinth 

-20 -10 0 

Breite / m 

10 20 

Dosisleistung / (Sv/h) 

5E-07 

100 

10 

1 

0.1 

0.01 

0.001 

1E-04 

1E-05 

1E-06 

1E-07 

1E-08 

1E-09 

1E-10 

1E-11 

1E-12 

Figure 21.1: Dose rates in the CBM experimental hall at a height of 2 m for a beam intensity of 10 9 Au ions per 

second at an energy of 30 AGeV. 

The iron beam dump is partly integrated in the back wall of the cave. The dose rate distribution at 

the beam dump was calculated separately in order to make use of a cylindrical symmetry. The dominant 

component of the dose at forward direction is due to muons. At the other directions the dose is dominated 

by the neutron component. According to the calculation, an iron beam dump of about 18 m length and 

diameters between 2 m and 7 m is required to absorb the radiation produced by a beam of 10 9 Au ions 

389

390 Radiation protection 

per second at an energy of 30 AGeV. The dose rate distribution at the dump is depicted in figure 21.2. 

Radius / m 

8 

6 

4 

2 

1 

concrete 

entry channel 

0.1 

0.01 

0.001 

0.0001 

1e-05 

iron 

1e-06 

1e-07 

0 

-9 -7 -5 -3 -1 1 3 5 

Länge / m 

7 9 11 13 15 

1e-08 

1e-09 

1e-10 

1e-11 

1e-12 

1e-13 

1e-14 

1e-15 

Dosisleistung / (Sv/h) 

Figure 21.2: Dose rate distribution at the CBM beam dump calculated with FLUKA for a beam intensity of 10 9 

Au ions per second at an energy of 30 AGeV.

Part V 

Organization and Responsibilities, 

Planning 

391

22 Planning and organization 

22.1 Cost estimates 

The overall cost estimate for the CBM experiment is summarized in table 22.1. The cost estimate of the 

CBM Silicon pixel and strip detector is based on the construction costs of similar detector components 

of ALICE. The cost range indicated for some of the items reflects different options where the decision 

is still open, for example the technology of the RICH photon detector, and the gaseous chambers of the 

Transition Radiation Detector (straw tubes, MWPC or GEM). The cost of the RPC has been estimated 

from the costs of the RPCs built for FOPI and ALICE. 

In addition to the uncertainties of the technology, the active area of the detectors and their number of 

channels are still subject of ongoing physics performance simulations. For example, hadron identification 

capability of CBM depends on the time-of-light distance, which influences (quadratically) the size of the 

second and third TRD, of the RPC and of the ECAL. This affects also the number of channels, as the cell 

size in the outer regions of the third TRD and of the RPC are limited by position resolution rather than 

by occupancy. The cost estimates for the TRD, the RPC and the ECAL listed in table 22.1 are based on 

a TOF distance of 10 m. 

Detector system Cost (M) 

Silicon Pixel Detector 1 - 1.5 

Silicon Strip Detector 7 

Ring Imaging Cherenkov Detector 6 - 10 

Transition Radiation Detector 8 - 10 

TOF stop detector (RPC) 5 - 7.4 

Electromagnetic Calorimeter + preshower 12.2 

Superconducting magnet 3 

Data acquisition and trigger 4 - 6 

Computing 3 - 6 

Infrastructure and installation 5 

Total detector cost 54.2 - 68.1 

22.2 Responsibilities 

Table 22.1: Cost estimate for the CBM experiment 

A preliminary sharing of the responsibilities for the different tasks and subsystems of CBM are listed 

in table 22.2. This sharing is based on the interest expressed by the institutes taking into account their 

expertise and ongoing R&D activities. The assignment of the responsibilities is neither complete nor 

final. In particular, we search for new collaborating institutes to participate in the activities and to take 

responsibilities. 

393


Work package Institution 

Simulation and analysis framework GSI Darmstadt 

Track, vertex and momentum recon- KIP Univ. Heidelberg, Univ. Mannheim, JINR-LHE 

struction 

Dubna, JINR-LIT Dubna 

Simulations hadron identification via Heidelberg Univ., Kiev Univ., NIPNE Bucharest, 

TOF, critical fluctuations 

INR Moscow, RBI Zagreb 

Feasibility study low-mass vector meson 

identification via dilepton pairs 

Univ. Krakow, JINR-LHE Dubna 

Feasibility study charmonium identifi- INR Moscow. GSI, PNPI St. Petersburg, GSI, RBI 

cation via dielectron and dimuon pairs Zagreb 

Feasibility study D-Meson identifica- GSI Darmstadt, Czech Acad. Science Rez, Techn. 

tion 

Univ. Prague, IReS Strasbourg 

Feasibility study hyperons Polytech. Univ. St. Petersburg, JINR-LHE Dubna 

Delta electrons GSI Darmstadt 

Silicon Pixel Detector IReS Strasbourg, Frankfurt Univ., GSI Darmstadt, 

RBI Zagreb, Krakow Univ. 

Silicon Strip Detector Univ. Obninsk, SINP Moscow State Univ., CKBM 

St. Petersburg, KRI St. Petersburg RPC 

TOF detector system with read-out LIP Coimbra, Univ. Santiago de Compostela, Univ. 

electronics 

Heidelberg, GSI Darmstadt, NIPNE Bucharest, INR 

Moscow, FZ Rossendorf, IHEP Protvino, ITEP 

Moscow, Korea Univ. 

Krakow, Univ. Marburg 

Seoul, RBI Zagreb, Univ. 

Transition Radiation Detector (TRD JINR-LHE Dubna, GSI Darmstadt, Univ. Münster, 

MWPC based) 

PNPI St. Petersburg, NIPNE Bucharest 

Transition Radiation Detector (TRD JINR-LPP Dubna, FZR Rossendorf, Tech. Univ. 

straw based) 

Warsaw 

Ring Imaging Cherenkov Detector IHEP Protvino, GSI Darmstadt, Pusan Nat. Univ., 

(RICH) 

PNPI St. Petersburg 

Electromagnetic Calorimeter (ECAL) ITEP Moscow, IHEP Protvino 

Forward Calorimeter INR Moscow 

Diamond Microstrip Start Detector GSI, Univ. Mannheim 

with read-out electronics 

Front-End Electronics, Trigger and KIP Univ. Heidelberg, Univ. Mannheim, JINR LIT 

Data Acquisition 

Dubna, GSI Darmstadt, Univ. Bergen, KFKI Budapest, 

Silesia Univ. Katowice, PNPI St. Petersburg, 

Univ. Warsaw 

Design of a superconducting dipole 

magnet 

JINR-LHE Dubna, GSI Darmstadt 

Calculation of radiation doses Kiev Univ. 

Modification of HADES for 8 AGeV Czech Acad. Science Rez 

Table 22.2: Responsibilities

22.3. Organization 395 

22.3 Organization 

According to its constitution from October 2004 the organization of CBM consists of the Collaboration 

Board (CB), the Management Board (MB), the Technical Board (TB) and the Physics Board (PB). 

The CB is the policy and decision making body of the CBM Collaboration. The CB is composed of 

one representative from each Member Institution. Each Institution has one vote in the CB. Members 

of the Management Board are ex-officio Members of the CB. The Chairperson of the CB is elected ad 

personam by the CB. 

The MB supervises the progress of the experiment, along the lines defined by the CB and prepares 

decisions for and makes recommendations to the CB. The MB review and act on recommendation of the 

Spokesperson and/or of Darmstadt Future Facility Management regarding all issues considered of major 

importance for the Collaboration. The MB may appoint review committees and task forces to provide 

advice on technical, scientific and technological decisions, as needed. 

The TB assesses all technical aspects of the CBM detector as decided by the CB. The design, coordination 

and technical execution of the experiment are discussed and agreed upon in the TB. Technical decisions, 

having important implications for the CBM detector must be presented to the CB for endorsement. The 

TB is composed of the Project Leaders (including Sub-project Leaders). Ex-officio members are the 

Spokesperson and Deputy and the other coordinators. The TB is chaired by the Technical Coordinator. 

The Technical Coordinator monitors and coordinates the activities of all the projects, and is responsible 

for the overall technical performance, planning and scheduling of the CBM detectors. 

The PB coordinates and assesses activities concerning physics topics of interest, including simulations 

and optimization of the physics performance of CBM. The PB is composed of the conveners and Coconveners 

of the physics performance working groups, who are nominated by the Spokesperson and 

confirmed by the MB. The PB is chaired by the Physics Coordinator. 

CBM has one Spokesperson. The Spokesperson is responsible to the Collaboration Board for the execution 

of the CBM project. The Spokesperson represents the CBM Collaboration to the Darmstadt Future 

Facility Committee, to its Management and to the outside world. The Spokesperson may appoint review 

committees and task forces to provide advice on technical, scientific and technological decisions, 

as needed. 

The Spokesperson, in consultation with the Management Board and the Darmstadt Future Facility Management, 

nominates for confirmation by the CB a number of Coordinators. The Coordinators report to 

the Spokesperson. 

The following persons have been elected up to now: 

Chairman of the Collaboration Board: M. Petrovici 

Spokesperson: P. Senger 

Technical Coordinator: W.F.J. Müller 

Physics Coordinator: V. Friese 

The structure of the Technical Board is shown in the upper part of figure 22.1. During the time period 

until the submission of the Technical Proposal the Physics Board is composed of the Physics Performance 

Working Groups as illustrated in the lower part of figure 22.1.


Technical Board Technical coordinator 

W.F.J. Müller 

Silicon Pixel 

RPC 

FEE/DAQ 

Silicon Strip 

ECAL 

HLT/offline 

Physics performance working groups 

Framework 

J/ψ → e+e- 

J/ψ → µ+µ- 

Tracking 

ρ, ω, φ → e+e- 

event by event 

Figure 22.1: 

RICH 

Start 

ECS 

Physics coordinator 

V. Friese 

hadron ID 

D meson 

TRD 

Magnet 

Cave 

Infrastructure 

Electron ID 

RICH 

Hyperons

22.4. Schedule 397 

22.4 Schedule 

This Technical Status Report still includes alternative possible technical solutions for the various requirements 

of the planned experiments. It will take two more years of intensive R&D in order to decide upon 

the layout of the experiment, the technology of the detectors, and on the trigger, DAQ, and FEE architecture. 

We plan to submit a Technical Proposal end of 2006. The detailed design of the CBM subsystems 

including prototyping leading to the Technical Design Reports will take another 3-5 years. For the construction 

of the components we foresee 2-3 years. Installation of the first detector components in the 

CBM cave will start in 2012. Commissioning of the experiment is planned for 2014 when the operation 

of SIS300 starts according to the present schedule. A global schedule for for the design, prototyping, 

construction, and installation of all CBM subsystems is presented in figure 22.4.


Nr. Vorgangsname 

1 CBM 

2 Definition of Buildings 

3 cave 

4 annex building 

5 simulation and analysis software 

6 simulations/feasibility studies 

7 design framework 

8 maintenance framework 

9 tracking in magnetic field 

10 vertex reconstruction 

11 momentum reconstruction 

12 global tracking 

13 hadron identification 

14 electron identification 

15 digitisers 

16 feasibility studies 

17 D-meson 

18 J/psi 

19 rho 

20 critical fluctuations 

21 hyperons 

22 SiliconTracking System (STS) 

23 Pixel detectors 

24 Simulations, layout 

25 Sensor 

26 R&D, design 

27 prototyping 

28 production 

29 Fast read-out 

30 design 

31 General 

32 Det. Control, supplies 

33 mechanics, cooling 

34 assembly and test 

35 Strip detectors 

36 Simulations, layout 

37 Sensor 



40 production 

41 FEE chip 



44 production 

45 General 

46 Mechanics, cooling 

47 Det control, supplies 

48 assembly and test 

Version vom Mi 22.12.04 WJacoby_D 

Struktur des Zukunftsprojekts Mi 22.12.04 

2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 201 

H2 H1 H2 H1 H2 H1 H2 H1 H2 H1 H2 H1 H2 H1 H2 H1 H2 H1 H2 H1 H2 H1 H2 H1 H2 H1 

Figure 22.2:

22.4. Schedule 399 


1 CBM 

2 RICH 

3 simulations and concept studies 

4 radiator 

5 simulation, vessel design 

6 prototyping and test 

7 construction 

8 mirror 

9 mirror design 


11 production 

12 photodetector 

13 UV detector R&D 

14 UV detector prototype 

15 UV detector production 

16 FEE 

17 design and prototyping 


19 general 

20 mainframe design and construction 

21 gas system design and construction 

22 HV supply design and construction 

23 assembly, installation, test 

24 Transition Radiation Detector 

25 simulations and concept studies 

26 radiator 

27 R&D 


29 production 

30 fast gas chambers 

31 R&D 


33 production 

34 gas system 

35 R&D and prototype 

36 production and test 

37 Electronics 

38 FEE ASICs 




42 readout board 




46 Mainframe 

47 gas system, design and construction 

48 installation and test 

Version vom Mi 22.12.04 WJacoby_D 

Struktur des Zukunftsprojekts Mi 22.12.04 

2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 201 


Figure 22.3:



1 CBM 

2 Resistive Plate Chamber Array 

3 concept studies and layout 

4 detector 

5 R&D module 

6 array prototyping 


8 Electronics 

9 FEE ASICs 




13 readout board 




17 Mainframe 

18 gas system, design and construction 

19 assembly, installation, test 

20 Electromagnetic Calorimeter 

21 concept studies and layout 

22 detector modules 



25 readout electronics 




29 assembly, installation 

30 Data Acquisition and trigger 



33 production 

34 installation 

35 test 

36 Experiment Control System 



39 implementation 

40 

41 Infrastructure 

42 cave supplies 



45 installation 

46 magnet supply 



49 Installation 

50 detector supply 




54 beamline 




58 vacuum system 




62 beamdump 




66 Technical Status Report 

67 submission 

68 Technical Proposal 

69 Submission 

Version vom Fr 07.01.05 WJacoby_D 

Struktur des Zukunftsprojekts Fr 07.01.05 

2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 201 


03.01. 

Figure 22.4: 

01.01.

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