here - Institute for Astronomy Umleitung

SCIENCE DOCUMENTS 

SECTION 

Title Version Reference Date Coordinator Page 

Executive 

summary and 

fast facts 1.0 MUSE-MEM-GEN-056 04/02/2004 R. Bacon 003/007 

Science case 1.3 MUSE-MEM-SCI-052 04/02/2004 R. Bacon 008/107 

Exposure time 

calculator and 

performance 

analysis 1.2 MUSE-MEM-SCI-051 15/01/2004 R. Bacon 108/165 

Science 

preparation and 

science team 

organisation 1.0 MUSE-MEM-SCI-053 02/02/04 R. Bacon 166/190 

Data analysis 

software 2.2 MUSE-MEM-SCI-054 29/01/2003 E. Emsellem 190/197

This report summarizes the results of the 

Phase A study of the Multi Unit 

Spectroscopic Explorer (MUSE), a second 

generation VLT panoramic integral-field 

spectrograph operating in the visible 

wavelength range. 

MUSE has a field of 1x1 arcmin², sampled 

at 0.2x0.2 arcsec (Wide Field Mode, 

hereafter WFM), and of 7.5x7.5 arcsec², 

sampled at 25 milli-arcsec (Narrow Field 

Mode, hereafter NFM), both assisted by 

adaptive optics. The simultaneous spectral 

range is 0.465-0.93 µm, at a resolution of 

R~3000. MUSE couples the discovery 

potential of a large imaging device to the 

measuring capabilities of a high-quality 

spectrograph, while taking advantage of 

the increased spatial resolution provided by 

adaptive optics. This makes MUSE a 

unique and tremendously powerful 

instrument for discovering and 

characterizing objects that lie beyond the 

reach of even the deepest imaging surveys. 

The most challenging scientific and 

technical application, and the most 

important driver for the instrument design, 

is the study of the progenitors of normal 

Simulated MUSE deep field. Galaxies are 

coloured according to their apparent redshift. 

Galaxies detected by their continuum (I AB < 26.7 ) 

and/or by their Ly α emission (Flux > 3.9 10 -19 

erg.s -1 .cm -2 ) are shown. 

Sampled area (in arcmin²) and sampled volume 

(comoving Mpc 3 ) of MUSE deep fields (red 

circles) versus the current Ly α surveys (cross). 

nearby galaxies out to redshifts beyond 6. 

These systems are extremely faint and can 

only be found by their Ly α emission. 

MUSE will be able detect these in large 

numbers (~15,000) through a set of nested 

surveys of different area and depth. The 

deepest survey will require very long 

integrations (80 hrs each field) and will 

reach a limiting flux of 3.9 

10 -19 erg.s -1 .cm -2 , a factor 100 better than is 

achieved currently with narrow band 

imaging. These surveys will 

simultaneously address the following 

science goals: (i) study of intrinsically faint 

galaxies at high redshift, including 

determination of their luminosity function 

and clustering properties, (ii) detection of 

Ly α emission out to the epoch of 

reionization, study of the cosmic web, and 

determination of the nature of the 

reionization, (iii) study of the physics of 

Lyman break galaxies, including their 

winds and feedback to the intergalactic 

medium, (iv) spatially resolved 

spectroscopy of luminous distant galaxies, 

including lensed objects (v) search of lateforming 

population III objects, (vi) study 

of active nuclei at intermediate and high 

redshifts, (vii) mapping of the growth of 

MUSE Phase A Executive Summary version 1.0 page 1/4

The median descendant mass of galaxies studied 

in various surveys. The deepest ongoing 

spectroscopic survey is the VDSS, which selects 

galaxies to I AB =26. It samples galaxies to a 

redshift of about 4.5 which are the precursors of 

current day-galaxies with typical masses of a few 

times 10 11 solar masses. MUSE goes a factor 10 

deeper and samples the precursors of Milky Way 

type galaxies all the way to a redshift of 6.7, the 

end of reionization. 

dark matter haloes, (viii) identification of 

very faint sources detected in other bands, 

and (ix) serendipitous discovery of new 

classes of objects. Multi-wavelength 

coverage of the same fields by MUSE, 

ALMA, and JWST will provide nearly all 

the measurements needed to answer the 

key questions of galaxy formation. 

At lower redshifts, MUSE will provide 

exquisite two-dimensional maps of the 

kinematics and stellar populations of 

normal, starburst, interacting and active 

galaxies in all environments out to well 

beyond the Coma cluster. These will reveal 

the internal substructure which is the fossil 

record of their formation, and probe the 

relationship between supermassive black 

holes and their host galaxy. MUSE will 

enable massive spectroscopy of the 

resolved stellar populations in the nearest 

galaxies, outperforming current 

capabilities by factors of over 100. This 

will revolutionize our understanding of 

stellar populations, provide a key 

complement to GAIA studies of the 

Galaxy, and a preview of what will be 

possible with an ELT. Observations of 

extended emission-line objects will probe, 

e.g., the physics of winds from accretion 

disks in young stellar objects, and galactic 

fountains, at a spatial resolution that 

exceeds that of HST. MUSE will also 

allow high-resolution spectroscopic 

monitoring of volcanic activity on Io, 

studies of the outer planets, and 

characterization of small Solar system 

bodies. 

Examples of southern nearby disk galaxies, 

suitable for a census of massive stars: NGC45, 

NGC55, NGC247, NGC253, NGC300, 

NGC7793 (left-right, top-bottom). The DSS 

frames subtend a FOV of 5x5arcmin 2 . 

The MUSE instrument design is 

innovative, and employs an advanced 

slicer with a combination of mirrors and 

mini-lens arrays. The fore-optics includes 

an optical derotator, a calibration unit, an 

atmospheric dispersion compensator (only 

in NFM) and splitting optics. This feeds 24 

identical modules: each composed of a 

slicer, a high-throughput spectrograph with 

a broad response volume phase 

holographic grating, and a 4kx4k red 

optimized CCD. The instrument achieves a 

high throughput with an average of 0.24 

end-to-end. The total detector area will 

have 403 million pixels. Prototype slicer 


and volume phase holographic grating 

have been manufactured and tested 1 . 

High spatial resolution is achieved with 

AO. The AO system for MUSE is 

developed in a companion project, together 

with ESO. MUSE will benefit greatly from 

the planned adaptive secondary for the 

VLT, but can also work with an 

independent AO system, and has key 

science applications even without AO. The 

availability of 4 laser guide stars will result 

in more than 70% sky coverage at the 

galactic pole. The MUSE NFM will 

provide a PSF with diffraction limited 

core, which will beat HST with up to 

10-30% Strehl ratio in the I-z band. In 

WFM it will provide 0.3 arcsec resolution 

over 1x1 arcmin² even in poor seeing. 

MUSE is robust, easy to operate, and 

maximizes open shutter time for science. 

There are no moving parts in the 24 

modular spectrographs. The optics is not 

sensitive to temperature changes, and the 

instrument has only two basic modes 

(WFM and NFM). Pointing with 1 arcsec 

accuracy is sufficient, and the square field 

of view minimizes the need for rotation. 

Pre-imaging is not required. 

Instrument overview at the VLT Nasmyth platform. 

The instrument will be able to reach 

extreme depths by means of long total 

integrations. This is possible due to the 

combination of an optical design that 

incorporates field and aperture stops to 

control stray light, high throughput, more 

than a factor 2 increase in encircled energy 

with AO, sufficient spectral resolution in 

the red (R=4000@0.93 µm) to allow 

observations between the atmospheric OH 

lines, with 74% of the red spectral range 

free of OH, and an extremely stable 

instrument fixed on the Nasmyth platform. 

Preparatory work carried out with 

SAURON and FORS demonstrate the 

feasibility of long integrations. 

Construction of MUSE will include 

development of a full data reduction 

system, consisting of a pipeline able to 

remove the instrument signature in almost 

real time, advanced quick look and 

vizualisation tools for 3D spectroscopy, 

advanced data analysis tools for 3D datamining, 

3D deconvolution, and optimal 

datacube summation. 

Artist view of MUSE WFM & AO fields of view 

1 Results of slicer tests are not available yet (4/2/04) 

The overall development strategy 

minimizes risk while maximizing scientific 

return, by taking advantage of the synergy 


with the slicer development for NIRSPEC 

on JWST, through the manufacture and 

successful testing of a prototype slicer. A 

complete spectrograph unit will be built as 

prototype, before ordering the full set of 

24. A dedicated and detailed AIT plan is in 

place. 

Multiple trade offs were performed in 

close collaboration with industry to 

minimize the cost of MUSE. The current 

design takes full advantage of modularity, 

so that the 24 spectrographs can be 

manufactured at low unit cost but deliver 

high performance. The total cost of MUSE 

will be 9.4 M€ of hardware and 147 FTE. 

A 3 year Phase B followed by 4 year phase 

C/D will allow delivery to Paranal in mid 

2011, perfectly in phase with the launch of 

JWST & GAIA, and the completion of 

ALMA. 

The MUSE Consortium consists of groups 

at Lyon (management, system, IFUs), 

Oxford (structure, fore-optics), Potsdam 

(calibration unit, software), Leiden 

(adaptive optics), Zurich (financial 

In preparation for the deep surveys planned for 

MUSE, a pilot programme has been developed 

using the SAURON IFU spectrograph. The figure 

shows the velocity structure of the z=3.1 Ly α halo 

“blob1” in SSA22. The image is colour coded to 

show Ly α emission that is red and blue shifted 

compared to the sub-mm source. 

The JWST/NIRSPEC IFU slicer prototype in 

test at CRAL 

contribution) and ESO (detectors). The 

Consortium has world-leading experience 

with pioneering, building, and operating 

integral-field spectrographs, including 

TIGER, OASIS, SAURON, GMOS-IFU, 

PMAS, and, in the future, SINFONI, 

SNIFS, and NIRSPEC-IFU. It has unique 

expertise in the development of highquality 

user-friendly data reduction 

software, and leads the Euro3D effort. The 

science team consists of instrumentalists, 

observers and theorists, and is carrying out 

various preparatory science programs, and 

has performed extensive simulations using 

state-of-the-art models of galaxies and 

galaxy formation to assess the performance 

of MUSE and optimize its design. The 

Consortium will provide 134 FTE of effort 

over 7 years, as well as a contribution of 

1.75 M€ to the cost of the hardware, and 

dedicated AIT facilities built in Lyon. 

The Phase A study demonstrates that 

MUSE has a very large discovery 

potential, outperforms e.g., VIMOS and 

FLAMES by factors of well over 100, and 

builds and extends the leading role that 

Europe has developed in integral-field 

spectroscopy. It maximizes the return from 

the developments in adaptive optics, will 

keep the VLT competitive for another 

decade by providing an invaluable 

complement to ALMA, JWST and GAIA, 

and is a key step towards instrumentation 

for an ELT. 


MUSE is a 2 nd generation instrument for the VLT 

Observational Parameters 

Spectral range (simultaneous) 0.465-0.93 µm 

Resolving power 

2000@0.46 µm 

4000@0.93 µm 

Wide Field Mode (WFM) 

Field of view 

1x1 arcmin² 

Spatial sampling 

0.2x0.2 arcsec² 

Spatial resolution (FWHM) 

0.3-0.4 arcsec 

Gain in ensquared energy within 2 

one pixel with respect to seeing 

Condition of operation with AO 70%-ile 

Sky coverage with AO 

70% at Galactic Pole 

Limiting magnitude in 80h 

I AB = 25.0 (R=3500) 

I AB = 26.7 (R=180) 

Limiting Flux in 80h 3.9 10 -19 erg.s -1 .cm -2 

Narrow Field Mode (NFM) 

Field of view 

7.5x7.5 arcsec² 

Spatial sampling 

0.025x0.025 arcsec² 

Spatial resolution (FWHM) 

0.030-0.050 arcsec 

Strehl ratio 10-30% 

Limiting Flux in 1h 2.3 10 -18 erg.s -1 .cm -2 

Limiting magnitude in 1h R AB = 22.3 

Instrument 

MUSE 

Type 

Concept 

Number of modules 24 

Detector 

Grating 

VPHG 

Limiting surface brightness in 1h R AB = 17.3 arcsec -2 

Multi Unit IFU 

Advanced Slicer 

CCD 4096x4096 

End-to-end throughput 0.24 

Number of pixels 403,000,000 

Adaptive Optics 

Concept 

Ground layer 

Sodium Lasers 4 

Deformable mirror 33x33 actuators 

Location 

Nasmyth platform 

Dimension 5x3,5x2.5 m 3 

Weight 

7,800 Kg 

Consortium 

Artist view of MUSE WFM & AO 

fields of view 

Schedule 

9/2002-2/2004 Phase A 

7/2004-5/2007 Phase B 

9/2007-2/2011 Phase C/D 

End 2011 Commissioning 

CRAL (Lyon) - PI 

AIP (Potsdam) 

ESO (Munich) 

ETH (Zurich) 

Oxford University 

Sterrewachte Leiden

Science Case 

Coordinator : R. Bacon 

Institute : CRAL 

Written by : R. Bacon, R. Bower, S. Cabrit, M. Cappellari, M. 

Carollo, F. Combes, R. Davies, J. Devriendt, E. 

Emsellem, M. Franx, G. Gilmore, B. Guiderdoni, 

B. Jungwiert, R. Mc Dermid, S. Morris, O. Le 

Fevre, S. Lilly, P. Pinet, M. Roth, M. Steinmetz, 

L. Wisotzki, T. de Zeeuw 

Reference : MUSE-MEM-SCI-052 

Issue : 1.3 

Date : 04/02/04 

File : Science_case.doc 

Distribution : ESO & Consortium 

History: 

• 0.1 – 03/01/04 – First assembly from science team draft texts 

• 0.2 – 12/01/04 – Update from science team 

• 0.25 – 13/01/04 – Minor update 

• 0.3 – 22/01/04 – Major Update 

• 1.0 – 30/01/04 – Almost final 

• 1.1 – 01/02/04 – Minor changes 

• 1.2 – 02/02/04 – Important polishing (Tim & Richard M) 

• 1.3 – 04/02/04 – Last corrections, general intro, phase A release

Title: Science Case 

Reference: MUSE-MEM-SCI-052 

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Date: 04/02/2004 

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Documents 

Reference documents 

AD1 ETC and performance analysis 

AD2 MUSE Top Instrumental Parameters 

MUSE-MEM-SCI-051 


Acronyms 

AD 

AO 

DF 

ESO 

ETC 

MDF 

MUSE 

NA 

NFM 

PSF 

R 

RD 

S/N 

SF 

UDF 

VLT 

WFM 

Applicable Document 


Deep Field 

European Southern Observatory 

Exposure Time Calculator 

Medium Deep Field 

Multi Unit Spectroscopic Explorer 

Not Applicable 

Narrow Field Mode 

Point Spread Function 

Spectral Resolving Power 

Reference Document 

Signal over noise 

Shallow Field 

Ultra Deep Field 

Very Large Telescope 

Wide Field Mode



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Documents.................................................................................................................................. 3 

Reference documents .............................................................................................................3 

Acronyms ................................................................................................................................... 3 

1. Introduction ........................................................................................................................ 5 

2. Formation of galaxies......................................................................................................... 6 

2.1. Introduction ................................................................................................................ 6 

2.2. High redshift Lyman alpha emitters......................................................................... 11 

2.3. Fluorescent emission and the cosmic web ............................................................... 20 

2.4. Reionization ............................................................................................................. 24 

2.5. Feedback processes and galaxy formation............................................................... 27 

2.6. Ultra-deep survey using strong gravitational lensing............................................... 32 

2.7. Resolved spectroscopy at intermediate redshift....................................................... 35 

2.8. Sunyaev-Zeldovich effect ........................................................................................ 39 

2.9. Late forming population III objects ......................................................................... 40 

2.10. Active galactic nuclei at intermediate and high redshifts .................................... 42 

2.11. The development of dark matter haloes ............................................................... 44 

2.12. Merger rate ........................................................................................................... 45 

2.13. Survey strategy..................................................................................................... 46 

2.14. A pan-chromatic view of galaxy formation ......................................................... 50 

3. Nearby galaxies................................................................................................................ 53 

3.1. Introduction .............................................................................................................. 53 

3.2. Supermassive black holes in nearby galaxies .......................................................... 53 

3.3. Kinematics and stellar populations .......................................................................... 57 

3.4. Interacting galaxies .................................................................................................. 62 

3.5. Star formation in nearby galaxies............................................................................. 63 

4. Stars and resolved stellar populations .............................................................................. 66 

4.1. Introduction .............................................................................................................. 66 

4.2. Early stages of stellar evolution ............................................................................... 66 

4.3. Massive spectroscopy of stellar fields: our Galaxy and the Magellanic Clouds...... 71 

4.4. Massive spectroscopy of stellar fields: The Local group and beyond ..................... 76 

5. Solar system ..................................................................................................................... 89 

5.1. Introduction .............................................................................................................. 89 

5.2. Galilean Satellites and Titan surfaces ...................................................................... 89 

5.3. Surface heterogeneities of the small bodies ............................................................. 91 

5.4. Temporal changes in Jupiter, Saturn, Uranus and Neptune ..................................... 92 

6. Serendipity ....................................................................................................................... 94 

7. Instrument requirements................................................................................................... 95 

7.1. "Formation of galaxies" science case....................................................................... 96 

7.2. "Nearby galaxies" science case ................................................................................ 96 

7.3. "Stars and resolved stellar populations" science case .............................................. 96 

7.4. "Solar system" science case ..................................................................................... 96 

8. Competitiveness ............................................................................................................... 97 

8.1. Introduction .............................................................................................................. 97 

8.2. Wide field IFU ......................................................................................................... 98 

8.3. High spatial resolution IFU...................................................................................... 99



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1. Introduction 

This document presents the science case for the Multi Unit Spectroscopic Explorer (MUSE). 

Four scientific areas have been explored: formation of galaxies, nearby galaxies science, stars 

and resolved stellar populations and solar system applications. The formation of galaxies 

(section 2) is the most challenging scientifically and technically, and has been used as the 

most important driver for the instrument design. It has been developed in depth, using as 

much as possible quantitative estimators taken either from the literature or from extensive 

numerical simulations. The nearby galaxy science (section 3) and the stars and stellar 

population (section 4) have been significantly enhanced with respect to the pre-phase A 

document. We have also added a new section on solar system science (section 5), although it 

has not been considered as a driver for the instrument. 

Apart from these science goals, we stress the large potential of serendipitous discoveries of 

MUSE (section 6). We then summarize instrument requirements in section 7 and discuss 

MUSE competitiveness with respect to existing or planned similar facilities in section 8.

2. Formation of galaxies 



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2.1. Introduction 

The advent of 10-m class telescopes and high throughput instrumentation has fully opened a 

new research area in astronomy: the study of the overall populations of galaxies at high 

redshifts. At redshift 3, when the universe was 15 % of its current age, and the typical 

separations between objects were 1/4 of the current values, the galaxies that are unveiled by 

the deep surveys like the HDFs seem to be quite different from the local, giant galaxies of the 

Hubble sequence. 

Whereas, in the early nineties, only extreme objects were known in the distant universe 1 , 

recent deep surveys based on broad-band magnitude selection can now collect large 

populations of galaxies from z=2 to z=3–4 (e.g., Steidel et al 1996, 1999, 2003, Madau et al. 

1996). The strong spectral features of the so-called Lyman-break galaxies allow us to derive 

fairly accurate photometric redshifts from precise photometry. Hence deep ground-based and 

HST photometry can be used to sample and study fainter galaxies than can be observed 

spectroscopically. The ground-based studies have produced a wealth of information which we 

are still slowly digesting: the correlation function of Lyman break galaxies (Giavalisco et al. 

1997), the existence of galactic size winds (Franx et al 1998, Pettini et al 1998, Shapley et al 

2003), the interaction of winds with the IGM, the photoionizing flux (Steidel et al, 1998), the 

rest-frame optical properties of Lyman break galaxies, their optical emission lines, etc. These 

pioneering studies are now being extended to larger samples: e.g., VDSS survey (Le Fevre et 

al, 2003), Cosmos survey (Scoville et al, 2003), in order to obtain better statistics, and to 

describe the environmental dependencies. These large surveys will yield the z=3–4 equivalent 

of the 2DF or Sloan survey at much lower redshift. 

However it is clear that the current ongoing and planned surveys have intrinsic limitations that 

follow naturally from the capabilities of current telescopes and instruments. In order to obtain 

large enough samples in a reasonable time, they use integration times of typically 2 hours, and 

sample galaxies to a magnitude of R AB =25.5 (e.g., Steidel et al 1999), with incompleteness 

setting in above that limit. The consequence of this limit is that we study only the bright end 

of the luminosity function, and probe only a factor 10 in luminosity under the brightest 

objects, in a regime where the luminosity function is still rising. Furthermore, the galaxies are 

a very biased population of the full population, with a very strong correlation function 

(Giavalisco et al 1998). Even though their number density is similar to that of nearby normal 

galaxies (Steidel et al 1996), they are likely not the progenitors of the latter. The clustering 

strength is expected to depend on the number density of the halos in which galaxies reside. 

Assuming a simple relation between galaxy brightness and halo mass, we expect that faint 

galaxies cluster less than bright galaxies, and that faint galaxies are the true progenitors of 

normal nearby galaxies. As Lyman-break galaxies seem to be the progenitors of current giant 

elliptical galaxies that reside in clusters (Baugh et al 1998), we need to go a factor of 10 

deeper in order to access the galaxies that are the progenitors of normal galaxies. 

1 Active nuclei with very strong emission lines



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The HDF studies actually go deeper but are limited to a small number of objects. For instance, 

with magnitude limits roughly corresponding to R AB 5 have R AB

However, the great disadvantage of 

the search by narrow-band filters is 

that these achieve lower spectral 

resolution, and hence less contrast 

between the emitter and the night 

sky. Furthermore, bright sky lines 

that are abundant in the red make 

narrow band searches extremely 

inefficient over most of the 

wavelength range except for a few 

gaps. Finally, objects selected by 

narrow band surveys still need 

spectroscopic follow-up, and have 

generally large interloper fractions. 

At the depth that we wish to reach 

here, spectroscopic follow-up by 

itself would be very time 

consuming, and hence the only 

strategy which can work is one 

where the galaxies are selected 

from the spectroscopy itself. The 

efficiency of this search technique 

is higher, as long as we have an 

efficient spectrograph with wide 

field of view. The key element for 



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MUSE limiting flux 

The limiting flux quoted in the following sections are for 

an unresolved source and S/N=5. Approximately 50% 

of source flux is recovered by integration over 0.6x0.6 

or 0.8x0.8 arcsec², depending on seeing conditions and 

use of AO. Assumptions, results and analysis are 

presented in the “ETC and performance analysis” 

document (RD1). 

Integ. Line magnitude I AB 

Time Flux Full R R/20 

SF 1 h 50.0 22.2 23.9 

MDF 10 h 11.0 23.9 25.5 

DF 80 h 3.9 25.0 26.7 

UDF 80 h 1.3 26.2 27.9 

Notes: 

• Unless explicitly specified, flux and magnitude 

are average value in the 0.6-0.93 µm 

wavelength range 

• Flux is in 10 -19 erg.s -1 .cm -2 units 

• Magnitude is given for full (R~3000) or low 

(R~150) spectral resolution 

• SF is without AO and median seeing conditions 

• MDF, DF and UDF are with AO and median 

seeing conditions 

• UDF assumes a factor 3 gain by lensing 

MUSE is that it is a giant Integral Field Unit (IFU) spectrograph, which can sample a full 1x1 

arcmin 2 field from 4650 Å to 9300 Å. Using an IFU spectrograph is by far the most efficient 

way to search for emission line objects. Furthermore, we optimize MUSE by incorporating a 

partial Adaptive Optics system, which enhances the efficiency by another factor of 2. The 

depth that can be reached with this instrument is a flux limit of 3.9 10 -19 ergs.s -1 cm -2 , for Ly α 

emitters between z=2.8 and z=6.7, in an integration time of 80 hours. This is a factor of 30 

deeper than what is currently achieved with ground-based facilities! The number of galaxies 

detected at this level is unknown, but we can extrapolate from previous surveys, or use model 

predictions. The first results of the narrow band surveys (e.g. Rhoads et al.) indicate quite 

steep counts for the Ly α emission line flux (in number per arcmin 2 and unit redshift) at 

redshifts 3.4, 4.8 and 5.7: N(>f)∝f -2 between 2 10 -16 ergs.s -1 cm -2 and 1.5 10 -17 ergs.s -1 cm -2 , 

which, if extrapolated to 3.9 10 -19 ergs.s -1 cm -2 would indicate a factor 1500 more sources. The 

current observed densities of a few to a few 0.1 sources arcmin -2 (∆z) -1 (between z~3.4 and 

5.7) will be transformed into several 100 sources arcmin -2 (∆z) -1 . Moreover, since it turns out 

that the Ly α luminosity function seems to be quite constant, this gain will simultaneously 

mean more objects with fainter luminosities and more remote distances. 

If we take this rough extrapolation at face value, we will find 300–1000 Ly α emitters at 

2.8



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integrate 4 times longer 2 because it does not have the AO unit. We could only observe 

continuum-selected objects, of which only 25% have sufficiently strong Ly α . Hence we would 

find 75–250 Ly α objects in 320 hours of integration time, 0.2–0.8 galaxy per hour, more than 

a factor of 10 lower. Hereafter, we will use a more refined model of galaxy formation which 

gives more conservative results (N(>f)∝f -1.7 ) below 1.5 10 -17 ergs.s -1 cm -2 . According to this 

model, MUSE will detect 300 objects in an 80-hour integration, which is in the low end of the 

above-mentioned estimate. VIMOS would also detect fewer objects. Hence these comparisons 

are fairly independent of the details of the galaxy population. 

At the time when MUSE comes on the VLT, 8-10 m class telescopes will have been in 

operation for 15 years. Hence the "quick-and-easy" projects will have been done already, and 

the telescopes will have to be operated in a new way to push the frontier. Whereas the largest 

survey being carried out now takes on the order of 50 nights, programs of even larger size will 

be more typical by 2011. Hence we should think in programs which can be done in many 

100's of hours of integration time, not 100 hours 3 . 

In the following we discuss the science in the field of galaxy formation that can be done with 

MUSE in 1000 hours 4 . This is not meant to be exhaustive, nor meant to be a claim by the 

MUSE team that they reserve the right to do this all by themselves. It shows applications of 

MUSE, whether done by the MUSE team, or the community. The survey is a staggered 

survey using different depths and area coverage. It uses exclusively the WFM and AO 

capabilities, except for the shallow survey which does not require AO. It consists of: 

• Shallow survey (SF), reaching a flux density of 5. 10 -18 erg.s -1 .cm -2 , and an area coverage 

of 200 arcmin 2 . 

• Medium deep survey (MDF), reaching a flux density of 1.1 10 -18 erg.s -1 .cm -2 , and an 

area coverage of 40 arcmin 2 . 

• Deep survey (DF), reaching a flux density of 3.9 10 -19 erg.s -1 .cm -2 , and an area coverage 

of 3 arcmin 2 

• Ultra deep survey (UDF), reaching a flux density of 1.3 10 -19 erg.s -1 .cm -2 for 0.6 arcmin 2 , 

using lensing clusters. 

These numbers are chosen based on our current knowledge and simulations. It is likely that 

the strategy will evolve with time. With the above-mentioned estimates based on current 

surveys, it is foreseen that MUSE will have detected more than about 15,000 2.8

The science that can be done with 

MUSE is very broad. It opens up a 

completely new regime for optical 

spectroscopy, both at high redshifts, 

and at low redshift. At high redshifts, 

we can finally sample the progenitors 

of normal galaxies like the Milky Way. 

We can measure the strength of the 

correlation function as a function of 

luminosity of the galaxy. We can 

finally sample galaxies out to z=6.7, 

beyond the regime where the universe 

becomes optically thick shortward of 

Ly α . We can identify the nature of 

objects which produce the last phase of 

re-ionization. We can do the 

equivalents of the studies done now for 

very bright galaxies at z=3, study the 

impact of giant galactic winds on the 

IGM, study the environments of 

AGN 5 . We get (for free) resolved 



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spectroscopy of bright Lyman break galaxies at z=3, and, obviously, at lower redshifts. In 

short, MUSE opens up a full area of research that would be inaccessible otherwise. 

In the next sections we discuss in more detail the science goals which can be addressed 

simultaneously with these surveys. It is organized as follows: In 2.2 we discuss the high 

redshift Ly α emitters, including determination of their luminosity function and clustering 

properties. In 2.3 we consider the study of the fluorescent emission and the cosmic web and 

in 2.4 the nature of the last phase of the reionization. In 2.5 feedback processes to the 

intergalactic medium are studied in relation with the physics of Lyman break galaxies. In 2.6 

we take advantage of strong lensing to improve the depth of the survey and to perform 

spatially resolved spectroscopy of luminous distant galaxies. In 2.7 we extend this study of 

spatially resolved galaxies to non lensed surveys at intermediate redshift. In 2.8 we discuss 

follow-up observations of Sunyaev-Zeldovich cluster and in 2.9 we propose to use MUSE to 

search for late-forming population III objects. In 2.10 the study of active nuclei at 

intermediate and high redshifts is presented and in 2.11 the mapping of the growth of dark 

matter haloes. Measure of the merger rate is discussed in 2.12. Finally the survey strategy is 

presented in 2.13, and the complementarities of MUSE with JWST, ALMA and other 

facilities are discussed in 2.14. 

References 

Ajiki et al. 2003, AJ, 126, 2091 

Ajiki et al. 2002, ApJ, 576, 25 

Baugh et al 1998, ApJ, 498, 504 

Cowie & Hu 1998, AJ, 115, 1319 

Figure 2-1: Sampled area (in arcmin²) and sampled 

volume (comoving Mpc 3 ) of MUSE deep fields (red 

circles) versus the current Ly α surveys 

(cross) discussed in Table 2-1 (section 2.2). 

5 If every L* galaxy has a black hole, than this phase may have been common, although short lived



Issue: 1.3 

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Ellis et al. 2001, ApJ, 560, 119 

Franx et al 1997, ApJ, 486, 75 

Giavalisco et al. 1998, ApJ; 503, 543 

Hu & McMahon 1996, ApJ, 459, 53 

Hu et al. 1999, ApJ, 522, 9 

Hu et al. 2002, ApJ, 576, 99 

Kodaira et al. 2003, PASJ, 55, 17 

Kudritzki et al. 2000, ApJ, 536, 19 

Le Fevre et al, 2003, The Messenger 111 

Madau et al, 1996, MNRAS, 283, 1388 

Pettini et al 1998, ApJ, 508, 539 

Rhoads et al. 2000, ApJ, 545, 85 

Rhoads et al. 2003, AJ, 125, 1006 

Scoville et al, 2003 (http://www.astro.caltech.edu/cosmos) 

Shapley et al., 2001, ApJ, 562, 37 

Stanway et al. 2004, ApJ, submitted, astroph/0312459 

Shapley et al 2003, ApJ, 588, 65 

Stiavelli et al. 2001, ApJ, 561, 37 

Steidel et al, 1996, ApJ, 462, L17 

Steidel et al, 1999, ApJ, 519, 1 


Steidel et al, 2001, ApJ, 546 


Taniguchi et al. 2003, ApJ, 585, 97 

Venemans et al. 2002, ApJ, 569, 11 

Weymann et al, 1998, 505, L95 

2.2. High redshift Lyman alpha emitters 

The main target of the MUSE surveys is to find and study the building blocks of the local, 

normal galaxies such as our Milky Way, at an epoch when the universe was typically 1 Gyr 

old. The observation of such objects will be of great value to clarify the way galaxies form. In 

the commonly accepted hierarchical picture, mass assembling is a long-timescale process, that 

starts early and goes on till the present time. Making the census of big and small objects in the 

early universe, when the cosmic age was 1 Gyr, and studying their properties, will set strong 

constraints on detailed models of hierarchical galaxy formation. In this prospect, the specific 

questions which one wants to address by studying this population of objects are the following: 

how did galaxies like our Milky Way assemble from small fragments? What are the stellar 

and gaseous masses of these fragments? What are the masses of the dark matter haloes they 

are hosted in? What are their typical star formation histories? 

The issue is to find an observational signature that is as efficient as possible to identify highredshift 

low-mass objects. In section 2.1, we have argued that high-redshift low-mass objects 

should be searched for in an emission-line survey, and that MUSE is a unique instrument to 

reach this goal. Hereafter, we try to refine our estimates of the MUSE efficiency with respect 

to current surveys, and we discuss the type of statistical studies that could be achieved.



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A search for Ly α emission was the 

first proposed method to find highredshift 

forming galaxies (also 

called "primeval galaxies", a term 

not in use anymore). Partridge & 

Peebles (1967) predicted that the 

monolithic collapse of a giant 

elliptical galaxy should produce a 

Ly α luminosity of 10 45 erg/s, which 

would be observed at a typical flux 

level of 10 -15 erg.s -1 .cm -2 . In spite 

of many systematic searches, such 

objects have not been observed, 

and only deeper surveys, at the 

current sensitivity of a few times 

10 -17 erg.s -1 .cm -2 , have unveiled a 

population of Ly α emitters with 

luminosities 10 42-43 erg/s. Such 

luminosities are expected in the 

hierarchical picture. However, the 

failure to find very strong Ly α 

emission is probably caused by 

many effects: the line is partly 

decreased by the photospheric 

absorption line in post-starburst 

stellar populations (Valls-Gabaud 

1993). Furthermore it can be 

scattered out to large distances by 

resonant scattering, reducing the 

surface brightness. Finally, any 

z ∆z Area 

arcm 

in 2 

Flux limit 

erg/s/cm 2 

Density 

arcmin - 

2 ∆z -1 Reference 

2.42 0.14 1260 2x10 -16 0.49 Stiavelli et al. 2001 

3.13 0.04 50 1.5x10 -17 4.0 Kudritzki et al. 

2000 

3.44 0.065 46 2x10 -17 3.3 Cowie and Hu 

1998 

3.44 0.065 46 1.5x10 -17 4.2 Hu et al. 1998 

3.72 0.22 132 3.9x10 -17 (?) 0.21 Fujita et al. 2003 

4.5 0.07 1160 2x10 -17 1–1.5 Rhoads et al. 2000, 

Rhoads & 

Malhotra 2001 

4.54 0.053 24 1.5x10 -17 4.2 Hu et al. 1998 

4.86 0.05 543 1.1x10 -17 (?) 3.2 Ouchi et al. 2003 

5.7 0.1 707 1.6x10 -17 0.11– 

0.15 

Rhoads & 

Malhotra. 2001, 

Rhoads et al. 2003 

5.7 0.1 720 1.4x10 -17 (?) 0.28 Ajiki et al. 2003 

5.7 0.1 918 2x10 -17 0.3 Hu et al. 2004 

5.7 1 0.029 1.9x10 -18 34 Ellis et al. 2001 

Table 2-1: A sampling of the observed surface density found for 

Ly α emitters sorted out by redshift. The estimate of surface density 

is typically based on 10–100 objects, with Poisson error bars at 

the 10–30 % level (at 1 σ). The flux limits labelled by a (?) have 

been derived from the luminosities of the published sources. Only 

candidate Ly α galaxies are found by most papers, and some 

authors suggest correction factors significantly lower than 1. The 

last line corresponds to an estimate based on a single lensed 

source. This value is quite uncertain, but it illustrates how common 

these faint objects should be. 

dust in the neutral, scattering gas will reduce the escaping fraction of photons drastically 

(Charlot & Fall 1991, 1993). The outflows that are seen in nearby starbursts, and distant 

galaxies affect the direction, and kinematic structure of Ly α photons. However, it is now clear 

that galactic winds and massive outflows, as well as infalling material, strongly help Ly α 

photons avoid absorption through resonant scattering in the dusty medium, and escape from 

the galaxies (Lequeux et al. 1995). As a consequence, the question remains open, and only 

more detailed surveys will set stronger constraints. 

Table 2-1 gathers the results of the main narrow-band Ly α surveys. The current surveys on a 

fraction of a square degree fields in thin redshift slices (typically ∆z =0.05) obtain a few ten to 

a few hundred candidates that need subsequent spectroscopic confirmation. The density of 

candidates is typically a few arcmin -2 per redshift interval of unity. For instance, at a typical 

flux level of 2 10 -17 erg s -1 cm -2 , the Large Area Lyman Alpha survey (LALA), has 225 

candidates in 0.31 deg 2 at z=4.5, obtained after 30h of exposure time (in five narrow-band 

filters), and the confirmation of each object requires about 1h of LRIS exposure time at the

Keck Telescope (Rhoads et al. 

2000). The confirmation rate of 

true Ly α galaxies, although 

based on very poor statistics, 

only amounts to 30 to 50 %, 

and leads to an effective 

observing cost of 2.3–3.8 h of 

observing time per confirmed 

object. With the HST, the 

Grism ACS Program for 

Extragalactic Science 

(GRAPES, Malhotra et al.), 

and the ACS Pure Parallel 

Lyman α Emission Survey 

(APPLES, Rhoads et al.) at a 

typical spectral resolution 

R=100, and a flux level of 6 10 - 

18 erg s -1 cm -2 , are foreseen to 

give respectively about 63-400 

Ly α galaxies, and 1000 Ly α 

galaxy candidates at 4

galaxies without ambiguity. 

Whereas redshifts based on a 

single emission line are 

generally uncertain, the Ly α 

line can be identified by the 

asymmetric line profiles that 

are found in almost all cases 

(see, e.g., Franx et al 1997, 

Weyman et al 1998). These are 

caused by a combination of 

effects: line broadening by 

large-scale flows, and resonant 

scattering, and absorption by 

outflowing neutral material. 

The first authors showed how 

at a spectral resolution R=1500 

the asymmetry could be 

detected in a weak object, and 

could be distinguished from 

redshifted [OII] 3727. The 

structure of the outflows can 

be studied and lower limits for 

the SFR can be derived from 

the detailed study of the line 

profiles. More information on 

morphology given by subarcsec 

spatial resolution may 

unveil signs of merging and 

interaction, which are expected 

in the paradigm of hierarchical 

formation. 

To estimate the efficiency of 

MUSE, we produce 

predictions of the number 

counts of Ly α emitters that 

will be found in MUSE deep 

Any 

continuum 

magnitude 



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Flux Limit (ergs/s/cm2) 

z 

2.8-6.7 2.8-4 4-6.7 

CURRENT 2x10 -17 5.3 4.5 0.8 

SHALLOW 5x10 -18 43.1 35.6 7.5 

MEDIUM 

DEEP 

1.1x10 -18 166 102 64 

DEEP 3.9x10 -19 287 152 135 

ULTRA 

DEEP 

1.3x10 -19 386 198 188 

I AB >26.7 DEEP 3.9x10 -19 275 141 134 

I AB >29 DEEP 3.9x10 -19 108 35 73 

I AB >31 DEEP 3.9x10 -19 16.2 2.7 13.5 

Table 2-2: Predictions of the number densities (per arcmin 2 ) of Ly α 

emitters in two redshift ranges according to the GalICS model 

(Hatton et al. 2003 and other papers of the series). The Ly α escape 

fraction is set at the value of 0.15, in order to fit the current surveys at 

2.10 -17 erg/s/cm 2 and z=3.4, where the statistics is good enough (3.3 

Ly α emitters arcmin -2 (∆z) -1 ). The fraction of objects lost due to 

coincidence of Ly α and OH emission has been taken into account. 

Flux Limit 2.8



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observations for the local and z=3 universe. They correspond to a flat or decreasing cosmic 

SFR at z>4, for which very little data is available. Their predictions should be considered as 

quite conservative lower values since it is pretty possible that the cosmic SFR strongly 

increases at z>4, maybe by a factor 10 as suggested by the analysis of Lanzetta et al. (2002). 

Ultimately, the solution to clarify this point is to obtain the data with MUSE. 

We assume that Ly α photons are produced by ionized regions (case B recombination). In such 

models, the description of the Ly α line is particularly delicate, because of the existence of 

stellar absorption after a starburst (due to A stars), and of resonant scattering. Only a small 

fraction of the emitted Ly α photons should escape from a homogeneous dusty medium. 

Nevertheless, Ly α emission is observed in local objects (such as Blue Compact Dwarfs) and 

high-redshift galaxies (Ly-break galaxies and Ly α galaxies found in wide-field, narrow-band 

surveys). It is thought that the existence of an expanding medium prevents Ly α photons from 

resonant scattering, and produces characteristic P-Cygni profiles. There is no simple 

modelling of such a process. At the current stage, we make predictions for two different 

models: (i) a fixed escape fraction f esc which would correspond to «holes» in the gas and dust 

distribution, and that is normalised in order to reproduce number counts obtained by current 

surveys; (ii) absorption in a dusty 

medium without resonant scattering 

(let’s say, if the velocity of the 

expanding medium is high enough to 

completely hamper resonant scattering). 

A more realistic model is clearly needed. 

It is foreseen that the interpretation of 

current Ly α surveys and the preparation 

of MUSE will foster theoretical activity 

on this point. 

There is very little observational 

constraint on the value of the Ly α escape 

fraction. Steidel et al. (2001) give f esc ≥ 

0.07–0.1 from Ly-break galaxy spectra, 

but it is very likely that stellar 

populations at higher redshifts are 

younger, less chemically evolved and 

less dusty. We choose to fix f esc in order 

to reproduce current Ly α surveys. At 

z=3.4, the observed surface density of 

reasonable confirmed objects at the 

2.10 -17 erg.s -1 .cm -2 flux level is 12,000 

deg -2 (∆z) –1 , that is, 3.3 arcmin -2 (∆z) –1 . 

This is obtained with f esc =0.15. This 

number appears reasonable in the light 

of more specific studies that take into 

account plausible values for the covering 

factor and metallicity (e.g. Haiman & 

Spaans 1999). The decrease of 

Figure 2-2: Predictions of faint galaxy counts in 

three redshift bins with the GalICS model. The 

escape fraction for Ly α photons is fixed in order to 

reproduce the current counts at a flux limit of 

2.10 -17 erg.s -1 .cm -2 in a redshift slice at z=3.4. Solid 

dots show the results of the recent surveys taken 

from Table 2-1 (error bars are only Poissonian). 

The effect of clustering on the cosmic variance 

appears from the scatter of the points. The model 

seems to scale fairly with redshift. The open 

triangle shows an estimate based on a single lensed 

source at a redshift z=5.7. The MUSE Deep Field 

flux limit is also shown



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metallicity expected at higher and higher redshifts should help increase this escape fraction, 

but we do not attempt to model this effect. In any case, it will produce more detectable 

sources. 

At the 2 10 -17 level, our standard GalICS model predicts a surface density of 2855 deg -2 (∆z) -1 

at z=4.5, and 444 deg -2 (∆z) –1 at z=5.7, in reasonable agreement with the data of the LALA 

survey (3600—5400 deg -2 (∆z) –1 at z=4.5 and 390–540 /deg 2 /∆z at z=5.7, see Rhoads et al. 

2000, Rhoads and Malhotra 2001, Rhoads et all. 2003), provided the uncertainty on the 

confirmation rate, but on the conservative side. The SUBARU survey at the 1.4 10 -17 flux 

level finds 1000 candidates deg -2 (∆z) -1 at z=5.7 (Ajiki et al. 2003) and argue that they are 

bona-fide Ly α emitters (although only 1/10 th is confirmed spectroscopically). The slope of the 

faint counts predicted by our models is N(>f) ∝ f -1.7 , more conservative than the estimate 

based on a rough extrapolation of the current data N(>f) ∝ f -2 . 

As shown in Table 2-2, MUSE will be 

able to increase significantly the 

detection rate of the most interesting 

objects at z>4. At 2.8



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6x10 -18 flux level of GRAPES, GalICS predicts about 85 galaxies at z>4 in the 11.3 arcmin 2 

field of view of the ACS, for about 60h of observing time (at a cost of 0.7 h per galaxy), a 

projection that is quite sensitive to the flux threshold, but lies on the conservative side of the 

estimates made by the GRAPES team. Of course, if it turns out that GRAPES gets more 

objects, the efficiency of MUSE will be correspondingly increased. In any case, this makes 

MUSE deep fields significantly more efficient 

than current narrow-band surveys, and even 

than HST deep surveys, to find bona-fide faint 

Ly α emitters. 

Moreover, according to these models, most of 

the objects detected by MUSE at 4



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and the SFR history within these objects. Under the assumption that 1 M sun /yr produces 10 42 

erg/s (Kennicutt 1988), and that only 15 % of these photons actually escape from the galaxies, 

the MUSE Ly α luminosity function will probe objects that emit 10 41 erg/s at z~5.7 (currently 

it gets down to 5 10 42 erg/s, Ajiki et al. 2003), that translates into SFRs lower than 1 M sun /yr, 

up to objects which emit 10 43 erg/s and have more than 100 M sun /yr. 

Figure 2-4: The median descendant mass of 

galaxies studied in various surveys. We used the 

GALICS simulations to construct a set of 

simulated galaxies at cosmological distances, and 

we applied the selection criteria appropriate to 

each of the surveys. For each selected galaxy the 

simulation was used to follow the merging tree 

and to derive the stellar mass at z=0. This is 

called the descendant mass. The deepest ongoing 

spectroscopic survey is the VDSS, which selects 

galaxies to I AB =26. It samples galaxies to a 

redshift of about 4.5 which are the precursors of 

current day-galaxies with typical masses of a few 

times 10 11 solar masses. MUSE goes a factor 10 

deeper and samples the precursors of Milky Way 

type galaxies all the way to a redshift of 6.7, the 

end of reionization. 

Figure 2-5: Predictions for the 2D 2-point 

correlation function with the GalICS model. 

The line shows the values measured in the 

SUBARU Deep Field at a flux level of 1.1 10 -17 

erg/s/cm 2 by Ouchi et al. (2003) in a thin 

redshift slice z=4.86±0.03, maybe dominated 

by a large-scale structure. The green dots show 

the predictions at this flux level in the redshift 

bin z=4.67



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Other properties can be measured for subsamples of the MUSE sources with complementary 

instruments such as JWST and ALMA, and subsequently SKA, to get a complete view on the 

extinction, gas and dust content (through the redshifted H α , CO, and HI 21 cm lines, and the 

submm/mm continuum emission (see more details in section 2-14 ). 

Even quite conservative assumptions on the expected sources demonstrate that MUSE will be 

the milestone instrument for the beginning of the next decade to study the properties of star 

forming galaxies in the distant universe. 

References 

Ajiki et al. 2003, AJ, 126, 2091 

Barton et al. 2004, ApJ, submitted, astroph/0310514 

Charlot & Fall 1991, ApJ, 378, 471 

Charlot & Fall 1993, ApJ, 415, 580 

Cole et al. 2000, MNRAS, 319, 168 

Cowie & Hu 1998, AJ, 115, 1319 

Devriendt et al. 1999, A&A, 350, 103 

Ellis et al. 2001, ApJ, 560, 119 

Franx et al 1997, ApJ, 486, 75 

Fujita et al. 2003, AJ, 125, 13 

Haiman & Spaans 1999, ApJ, 518, 138 

Hatton et al. 2003, MNRAS, 343, 75 

Hu et al. 1998, ApJ, 502, 99 

Kennicutt 1988, ApJ, 334, 144 

Kudritzki et al. 2000, ApJ, 536, 19 

Lanzetta et al. 2002, ApJ, 570, 492 

Lequeux et al. 1995, A&A, 301, 18 

Ouchi et al. 2003, ApJ, 582, 60 

Partridge & Peebles, 1967, ApJ, 147, 868 

Rhoads et al. 2000, ApJ, 545, 85 

Rhoads & Malhotra 2001, ApJ, 563, 5 

Rhoads et al. 2003, AJ, 125, 1006 

Shimasaku et al. 2003, ApJ, 586, 111 

Silva et al. 1998, ApJ, 509, 103 

Steidel et al. 2001, ApJ, 546 

Stiavelli et al. 2001, ApJ, 561, 37 

Valls-Gabaud 1993, ApJ, 419, 7 

Venemans et al. 2002, ApJ, 569, 11 

Weymann et al 1998, ApJ, 505, L95



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2.3. Fluorescent emission and the cosmic web 

For the past three decades, analytic work (Zeldovich 1970) and cosmological simulations (e.g. 

White et al 1987, Evrard et al 1994, Furlanetto 2003) have been predicting that the first 

structures to form in the Universe are tangled in what has been dubbed the “cosmic web”. The 

picture of matter collapsing into moderately overdense sheets and filaments, with galaxies and 

galaxy clusters forming through continued collapse at their intersections has become common 

knowledge in the astronomical community. Yet, direct mapping of the cosmic web remains a 

challenging observational venture, especially at z ≥ 3. 

Several methods have been used to achieve this goal, from poking lines of sight to probe 

integrated one-dimensional properties (Damped Ly α system or Ly α forest studies as in e.g. 

Rauch 1998), to tri-dimensional surveys of galaxy populations using broad band filters 

(Lyman Break Galaxies e.g. Steidel et al 1999). The former method covers by construction a 

very limited portion of the sky since it relies on absorption behind an existing mesh of 

background quasars which are not spaced closely enough to yield information on a typical 

filament scale. As for the latter, since it only allows one to detect the brightest of sources, it 

also suffers from sparse sampling of the filamentary structures. 

However, galaxy candidates selected 

for their Ly α emission using deep 

narrow band filters are already 

known to sample the high redshift 

galaxy population at much fainter 

levels than studies based on 

continuum emission such as LBGs 

(see e.g. Steidel et al 2000). 

Therefore, one naturally expects a 

better sampling of the high redshift 

cosmic structures with such a 

technique. As a matter of fact, the 

first detection of a z = 3.04 filament 

by Moller & Fynbo (2001) has 

demonstrated the power of using 

Ly α emission to map the cosmic web. 

Moreover, mapping out filaments in 

their own Ly α light by finding 

enough star forming knots, allows 

one not only to probe the small 

fraction of baryons embedded inside 

galaxies but also the more diffuse 

emission coming from the nearby 

densest portions of the Inter Galactic 

Medium. In fact, existing analytic 

Figure 2-6 : Cosmological simulations of galaxy formation, 

with two phases of the baryonic gas taken into account(warm 

and hot phase with SPH, cold and molecular phase with 

sticky particles). At top is shown the dark matter particules, 

then the warm gas, cold gas, and the stars formed out the 

cold phase at bottom. From left to right are shown 4 

successive zooms (from Semelin & Combes 2003).



Issue: 1.3 

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estimates (e.g. Gould & Weinberg 1996) suggest that the surface brightness of such regions 

which lie within filaments but outside of galaxies, could be substantial. In other words, Ly α 

emission should offer a better map of the gas distribution in filaments than galaxy surveys do. 

Furthermore, as pointed out by Haiman & Rees (2001), diffuse Ly α -emitting gas constitutes 

an intrinsically interesting phase: this gas is cooling onto virialized halos, thus offering a 

unique opportunity to study the growth of bound objects. 

The technique of selecting galaxy candidates in narrow band filters which was briefly 

described in the previous section is costly as it involves doing ultra deep imaging and then 

going to large telescopes to get a spectroscopic confirmation of the redshift. This makes 

MUSE highly competitive since the redshifts are obtained simultaneously across the entire 

field. In addition, the flux limit of 1.1 × 10 -17 erg s -1 cm -2 reached by Moller & Fynbo (2001) 

to detect their z = 3.04 filament is well above what MUSE will reach, either in shallow or 

deep mode (5 × 10 -18 erg s -1 cm -2 and 3.9 × 10 -19 erg s -1 cm -2 respectively) so that one could 

imagine doing a shallow survey covering quite a large portion of the sky without the adaptive 

optics, and still gain a lot as compared to current narrow band filter surveys (as e.g. LALA) in 

terms of mapping the cosmic web at high redshift. As an example, it is necessary to get 

accurate redshifts of objects to determine their membership in groups or filaments. Such 

redshifts will be obtained without further overhead from such a MUSE survey. Moreover, all 

currently planned Ly α surveys target a redshift window 4 < z < 5 (LALA or the SUBARU 

survey) or z~2 (NOT survey) and none cover the range z~3 where MUSE will have 

significant sensitivity. Finally, as pointed out by Weidinger at al (2002) a decent size filament 

survey containing a few tens of objects could also be used to meaningfully constrain the value 

of Ω Λ , in a way that intersects the probability curves from the various SN Ia cosmology 

projects. Such constraints would be coming from a modified Alcock-Paczy’nski (1979) test, 

involving statistical geometrical properties of filaments (lengths, radii and angles). We refer 

to the original work of Weidinger et al (2002) for details on this specific aspect. 

Finally, to illustrate the most unique point of using an instrument such as MUSE, we take 

advantage of the recent work of Furlanetto et al (2003) who explored the possible detection of 

filaments through the detection of diffuse Ly α emission at low (z = 0.15) redshift using high 

resolution hydrodynamical simulations. Their study is summarized in figure 2-7 which shows 

a surface brightness map of a large portion of the cosmic web as well as the blow up of one of 

its filaments. As Kunth et al (2003) rightly argue, properly computing Ly α emission from starforming 

galaxies is a strenuous task, but one can nevertheless obtain simple empirical 

estimates. Taking those of Furlanetto et al (2003) at face value (panel (b) of figure 1), the 

scale on the plot is easily converted into units of erg s -1 cm -2 arcsec -2 by a simple subtraction 

of 21.5 from the given colour tables, so that the bottom of the colour scale on the right hand 

side of the figure is simply 3 × 10 -21 erg s -1 cm -2 arcsec -2 . Assuming that the filament remains 

unchanged at z = 3, and simply taking into account the cosmological dimming factor for 

surface brightness which scales like (1 + z) -4 , and the shrinking of apparent diameter, one 

realizes that MUSE in deep field mode (whether in AO mode or not) should be able to 

marginally detect such diffuse Ly α emission at z = 3. (See the calculation below on 

fluorescence for some more detailed numbers).



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Figure 2-7: Maps of Ly α surface brightness Φ for z = 0.15 and ∆z = 10 -3 (∆λ = 1.2 Å) taken from [3]. Panel 

(a) assumes an angular resolution of ~ 29''; the rest show the region outlined in green with 7.2'' resolution 

(or ~ 13 h -1 kpc). Except for panel (a), pixels with Φ < 10 photons cm -2 s -1 sr -1 are excluded. (a), (b): 

Fiducial model. (c): ε α = 0 for self-shielded gas. (d): Estimated Ly-α emission from star formation in the 

slice (such emission was not included in the other panels). 

Several authors have calculated the expected surface brightness of optically thick neutral gas 

illuminated by the UV background (i.e. without enhancement by a local UV source such as 

embedded star formation). In figure 2-8 we show an updated version of the calculation of 

Gould & Weinberg (1996), using a recent estimate of the evolution of the UV background 

(Haardt, Private Comunication, CUBA code). As can be seen, the surface brightness of all 

optically thick neutral gas in the universe is close to the MUSE detection limit at z=3 if one 

could sum over the 10x10 arcsec characteristic radius of these optically thick clouds. 

Obviously the feasibility of such an observation will depend strongly on whether systematic 

errors begin to dominate when searching for such low signals over relatively large numbers of 

spatial samples. 

Given an estimated characteristic 

size for Lyman Edge absorbers, 

one can estimate their space 

density as a function of redshift. 

For a 50 kpc size (corresponding to 

approximately 10 arcsec at high 

redshifts), the resulting space 

density is roughly 3.5 x 10 -3 Mpc -3 . 

Combining this with the area 

coverage of MUSE, one can 

estimate that there should be 

roughly 10 such systems in a 

MUSE field of view between 

3



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Preparation). This extends the results from Moller & Fynbo (2001) to z=3. The peak in PDF 

at higher fluxes corresponds roughly to the values calculated in Figure 2-8, supporting the 

idea that large areas may ‘light up’ if these surface brightnesses can be detected. 

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Evrard, A. E., Summers, F. J., & Davis M. 1994, ApJ, 422, 11 

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Gould, A., & Weinberg, D. H. 1996, ApJ, 468, 462 

Haiman, Z. & Rees, M. J. 2001, ApJ, 556, 87 

Kunth, D. et al. 2003, ApJ, in press, astro-ph/0307555 

Moller, P., & Fynbo, J. U. 2001, A&A, 372, L57 

Rauch, M. 1998, ARA&A, 36, 267 

Steidel, C. C. et al. 1999, ApJ, 519, 1 

Steidel, C. C. et al. 2000, ApJ, 532, 170 

Weidinger, M., M˜oller, P., Fynbo, J. U., Thomsen, B., & Egholm, M.P. 2002, A&A, 391, 13 

White, S. D. M., Frenk, C. S., Davis, M., & Efstathiou, G. 1987, ApJ, 313, 505 

Zel'dovich, Ya. B. 1970, A&A, 5, 84 3



Issue: 1.3 

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2.4. Reionization 

Because of the absence of any significant continuum absorption blueward of Ly α in the 

spectra of QSOs at redshifts as high as z≥5 (the so called Gunn-Peterson trough) hydrogen in 

the universe is highly ionized (i.e. to better than 1 part in 10000) for redshifts below 5. It is 

usually assumed that the hard spectra of QSOs and the softer spectra of hot stars are the prime 

ionizing sources at these redshifts. A similar analysis for Helium indicates that at redshifts 

below z≈3, Helium is also double ionized, whereas at higher redshift Helium ionization is at 

best only patchy. The transition in the opacity of HeII at redshift 3-4 coincides with an 

obvious jump in the temperature of the intergalactic medium (IGM) by a factor of ~2 (Theuns 

et al. 2002). This result is usually interpreted by the transition of a soft UV radiation field at 

z>4 that is incapable of ionizing HeII and that is dominated by stars, to a hard, HeII-ionizing 

UV radiation field with significant or even dominating contributions from QSOs at z6 in the SDSS followed by their 

spectroscopy is providing invaluable clues to the evolution of the universe at its earliest 

epochs. Even though QSOs at z=6.5 are far to rare to contribute significantly to the UV 

background, the mere presence of a luminous QSO at z=6.5, i.e. an epoch when the universe 

was less than 1 Gyr old, is indicative that massive structures (including supermassive black 

holes) must have formed at very early cosmological epochs. Within the concordance ΛCDM 

scenario such luminous QSOs would be hosted in halos with masses of 10 13 M sun . Even more 

excitingly, however, these QSO show clear indication of continuum absorption blue ward of 

Ly α corresponding to a neutral hydrogen fraction of ≥1% (by mass) and ≥ 0.1% (by volume), 

respectively. The standard interpretation is that we are witnessing the latest stages of the 

reionization epoch. According to numerical simulations of the propagation of ionizing 

photons in a ΛCDM universe (Gnedin 2000), the final stages of the reionization of the 

universe (the percolation phase) should happen almost instantaneously with a sharp transition 

from almost neutral to an almost fully ionized stage. Due to the strongly changing optical 

depth, a strong evolution of the abundance and the properties of so-called Ly α -emitters, the 

likely source of ionizing photons, is expected (see below). The red wavelength range of 

MUSE (probing Ly α at the redshift interval z=6-6.7) is well adapted for this crucial 

cosmological epoch. 

A second, very interesting result has been recently provided by the cosmic microwave 

background satellite, WMAP. Cross correlations between the temperature and polarization 

measurements of the CMB indicate that the total optical depth of free electrons is τ≈0.17, 

while a suddenly reionization at z=6.5 results in a significantly lower optical depth of τ≈0.04. 

However, the WMAP results are still preliminary as they are based on a cross correlation 

between the temperature and polarization measurements only. A detailed analysis of the 

polarization power spectrum is still awaiting publication. Nevertheless, should the WMAP 

polarization measurement be confirmed, it will highlight a very interesting inconsistency 

between IGM and CMB data. Unfortunately, the CMB polarization signal is only providing a



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global constraint that contributes little to the detailed time evolution of the reionization. 

Hydrogen must be essentially neutral over a substantial fraction of the z>6 universe, but the 

exact epoch is largely unconstrained. Using semi analytical and numerical models, the 

WMAP and QSO results can be put in concordance, if the universe experiences a very 

extended reionization history starting at z≈20 and ending at z≈6. However, the apparent high 

temperature of the IGM provides evidence that the energy associated with the reionization 

was invested at relatively late cosmic epochs (z6 are the ideal tests to further constrain the reionization history 

of the Universe because (i) they are the likely sources of ionizing UV photons and (ii) their 

abundance as a function of redshift directly probes the ionization state of the IGM. Even small 

fractions of neutral hydrogen are efficient in scattering Ly α photons in direction and 

frequency thus making faint emitters unobservable, unless the intrinsic line width is very large 

According to the simulations detailed in section 2.2 we expect 27 Ly α -emitters in a single 

MUSE deep field in the 6–6.7 redshift range. In the proposed survey of 5 deep fields (see 

section 2.12.1) we shall get 135 objects in total. By investigating their evolution, the 

reionization history can be probed near x HI ≈1, a regime that is poorly tested by Gunn- 

Peterson-trough measurements. 

Two main signatures that can be uniquely searched for with MUSE are the following: 

• If the neutral fraction falls considerably below levels of 1%, then the partly neutral 

IGM produces a Ly α damping wing that should absorb a significant part of the Ly α 

line, if the HII region produced by the galaxy around itself is not large enough to 

move the damping wing far away from the line center. Faint Ly α emitters should 

'disappear' rapidly beyond the reionization redshift (Haiman & Spaans 1999), while 

the brighter sources would still be visible (Cen & Haiman 2002). The luminosity 

function and its redshift evolution of the Ly α emitters contain information on the 

neutral fraction and thus on reionization. Furthermore, any cosmic structure of larger 

than average density will induce infall of the IGM around it, i.e. the gas will “see” the 

source emission shifted to the blue, and absorbing red-ward of the Ly α line center; this 

effect may potentially wipe out most of the line. The optical depth owing to this effect 

increases roughly as (1+z) 4 (Haiman & Loeb 2002). 

• There should be characteristic imprints from a partially neutral IGM on the Ly α line 

shape. This effect is hard to observe in a single source, but can be measured if one has 

a statistical samples of ≥ 100 Ly α emitters (argued in Haiman 2003) as provided by 

the MUSE deep fields.



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Figure 2-9 Left: The suppression of the total line flux relative to the unobscured line as a function of the 

star formation rate of a galaxy at z=6.56. Right: The suppression of the total line flux relative to the 

unobscured line as a function of line width. The top lines corresponds to a model with a proper HII region 

size of 0.7 Mpc (f esc =1), the bottom curve describes a Ly α line model without any HII region (from 

Haiman 2003). 

The second effect is illustrated in Fig. 2-9. Prior to reionization, the line profile correlates 

with the size of the local HII region and therefore with the luminosity and age of the source as 

well as with the intrinsic line profile. For example a line as narrow as ~30 km.s -1 would 

essentially be erased if the star formation rate is below 1 M sun yr -1 , but for line as broad as 300 

km.s -1 , 20% of the total flux would be transmitted for arbitrarily faint sources. The detailed 

imprints of the reionization history cannot be disentangled in narrow-band Ly α surveys that 

only trace the brightest objects of the population, but require a survey that probes the number 

function as well as the line profiles, as provided by the MUSE deep fields. 

At the moment, theoretical models provide little quantitative constraints on the observable 

significance of these effects and to what extend they are clearly observable with MUSE. For 

example, current model estimates of the probability of Ly α photons to penetrate the z>6 IGM 

range from 0.001 to ~1, a fact that demonstrates the large uncertainties involved in these 

estimates. The fraction of Ly α photons that manage to escape the star-forming galaxy is 

likewise poorly constrained. Besides providing a detailed understanding of the reionization 

history of the universe, MUSE has the potential to shed light on the details of the star 

formation, the IMF, and the radiation transport in this first generation of galaxies. 

References 

Theuns et al. 2002, ApJ, 567, 103 

Gnedin 2000, ApJ, 542, 535 

Haiman & Spaans 1999, ApJ, 518, 138 

Cen & Haiman 2002, ApJ, 570, 457 

Haiman & Loeb 2002, ApJ, 576, 1 

Haiman 2003, ApJ, 595, 1



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2.5. Feedback processes and galaxy formation 

Our understanding of galaxy formation is making rapid advances. Improvements in our 

knowledge of the basic cosmological parameters (Ω 0 , H 0 , Γ 0 , etc., Bennett et al., 2003), and 

developments in computer simulation techniques mean that we are able to accurately trace the 

collapse of dark matter structure (eg., Jenkins et al. 2001). The outstanding difficulty is now 

to understand how the baryonic component collapses down to form galaxies. Computer 

simulations that incorporate gas cooling lead to the formation of far too many small galaxies 

in the early universe. While they predict that more than 50% of baryons are able to cool and 

form into stars, the observed fraction is only 8%! This problem is often referred to as the 

cosmic cooling crisis (White & Rees, 1978, Cen & Ostriker, 1993, Balogh et al., 2001) and is 

closely related to the angular momentum problem that causes model galaxies to be too small 

(Navarro & Steinmetz, 1997). 

The reason for this crisis is that these simulations lack effective feedback. Clearly it is not 

enough to simply let the gas cool, the 

rate of cooling must be balanced by the 

injection of energy from SNe or AGN. 

But while the idea is widely accepted, 

the actual mechanism is poorly 

understood, and even more poorly 

constrained by observations. One of 

the most popular explanations is superwinds: 

high power blast-waves that 

sweep nascent galaxies clean of their 

interstellar medium, driving it to 

distances of 0.5 - co-moving Mpc, so 

that the ejected gas is never able to 

collapse back onto the proto-galaxy 

(Springel & Hernquist, 2003, Benson 

et al.2003). The idea is appealing 

because it would also explain the 

wide-spread distribution of metals 

through-out the universe (Theuns et 

al., 2002) and may resolve the spiral 

disk angular momentum problem too. 

However, observational support for 

superwinds is tantalising but elusive. 

Local analogues for high power 

superwinds may exist in dwarf star 

burst galaxies (such as M82, Martin 

1999). In these galaxies, a powerful 

star burst drives some of ISM out of 

the galaxy at speeds of up to 600 km/s. 

However (1) these galaxies are dwarfs 

Fig 2-10: The velocity structure of the Ly α halo “blob1” in 

SSA22 from observations with SAURON on the WHT (Bower et 

al., 2003). The image is colour coded to show Ly α emission 

that is red and blue shifted compared to the sub-mm source. In 

addition to the complex dynamics of the halo of the submm 

source, the two lyman break galaxies in the field also have 

distinct emission line haloes. This is most clearly seen for the 

C15 source. This halo has a velocity shear across it that 

suggest the ionised gas is being expelled in a bipolar outflow. 

The flow is similar to local star burst galaxies except that it is 

much more luminous.

- it is unclear that the gas 

would escape from a larger 

galaxy, (2) little mass is 

involved in this wind, 

insufficient to explain the 

cosmic cooling crisis. 

At high redshift, superwinds 

may be more widespread and 

even more powerful. Evidence 

for high-z superwinds comes 

from comparing the redshifts 

of galaxies measured from Ly α , 

nebular lines (in the observed 

IR) and ISM absorption lines. 

These redshifts are often 

discrepant at the level of 300 

km/s, which Pettini et al. 

(1998; Shapley et al 2001, Fig. 

1.8) interpret as a P-cygni 

effect in the super-wind 

outflow. It is not clear, 

however, whether this wind 

will actually escape the galaxy 

(or fall back in a galactic 

fountain), nor whether the wind 

includes substantial fraction of 

the galaxy's baryonic mass. To 

answer these questions we 

need to look at how far from 

the galaxy the wind extends. 

We would like to see if the 

super-winds create hot 

outflowing bubbles around the 

proto-galaxies. 



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The SAURON deep field 

In preparation for the deep surveys planned for MUSE, a pilot 

programme has been developed using the SAURON IFU 

spectrograph (Bacon et al. 2001). We have now surveyed 

three fields, with a paper (Bower et al, MNRAS) describing 

the first of these now in press. We have studied the structure 

of the Ly α emission-line halo, LAB1, surrounding the submillimetre 

galaxy SMM J221726+0013. The field (41×33 

arcsec² sampled at 0.95 arcsec) was observed for a total of 9 

hours split in 30 mn exposures. The measured limiting 

surface brightness is found to be from 1×10 −18 erg s −1 cm −2 

per sq. arcsec for lines with σ = 2Å to 3.5×10 −18 erg s −1 cm −2 

per sq. arcsec for lines with σ = 20Å. The observations trace 

the emission halo out to almost 100 kpc from the submillimetre 

source and identify two distinct Ly α “mini-haloes” 

around the nearby Lyman-break galaxies. The main emission 

region has a broad line profile, with variations in the line 

profile seeming chaotic and lacking evidence for a coherent 

velocity structure. Around the Lyman-break galaxy C15, the 

emission line is narrower, and a clear shear in the emission 

wavelength is seen. A plausible explanation for the line 

profile is that the emission gas is expelled from C15 in a 

bipolar outflow, similar to that seem in M82. 

The second field is centered on a bright QSO (HB89- 

1738+350). This is a V=20.5, z=3.239 QSO chosen so that its 

rest frame Ly α would be inside the small SAURON spectral 

range, but also so that the cube would include a significant 

range where intervening absorption systems seen along the 

line of sight to the QSO could be correlated with any 

emission line objects found in the SAURON cube. More 

recently an even deeper exposure of 20 hours on the second 

Ly α peak in SSA22 has been obtained. Analysis is in 

progress. 

Although SAURON was not optimized for this type of 

science, it demonstrates the capabilities of IFU for deep 

fields. 

The MUSE instrument will play a crucial role in determining the nature of feedback in high 

redshift galaxies; and thus solving one of the most pressing problems in extra-galactic 

astronomy. 

2.5.1. Understanding Feedback with Spatially Resolved Galaxies 

With the high spatial sampling, sources brighter than 3.9 10 -18 erg.cm -2 .s -1 will have Ly α 

emission that can be spatially resolved. There will be ~60 such sources in each 80 hour 

pointing. In the local star burst galaxies, the material being ejected from the galaxy is seen as 

a bipolar outflow. Our observations of the diffuse halo of Lyman break galaxy C15 in the



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SSA22 proto-cluster (z=3.09) hint that the same is likely to be true in high redshift systems 

(Fig 2-10). 

The geometry of this flow is an important constraint. Is the galaxy formation process 

terminated by an explosion that drives a near spherical shell, or is there a balance between a 

bi-polar outflow and continued inflow along orthogonal directions? These are key questions 

that we can compare to numerical simulations that are being developed to model super-wind 

out-flows in proto-galaxies. 

Each pointing will identify ~15 objects with continuum magnitudes brighter than I AB =23, we 

will detect the rest-frame UV continuum with sufficient s/n to map the velocity structure of 

the neutral ISM through absorption lines such as SiII. This will allow us to map the structure 

of both the emitting and absorbing material, making a very detailed test of the geometry of the 

outflow/inflow and thus allowing us to assess the balance of cooling and feedback. 

2.5.2. Measuring Feedback with QSO sightlines 

A powerful technique for studying the larger scale impact of feedback is to target fields 

containing QSOs that are sufficiently bright for absorption line studies. In this way, the 

redshifts of the Ly α emitters can be compared to absorption features in the QSO spectrum 

(often referred to as the Ly α forest), allowing us to probe the association between the young 

galaxies and the neutral gas from which they form. 

Adelberger et al., (2003) performed this experiment using Lyman-break galaxies at z~3. 

For galaxies with a large separation (>2 co-moving Mpc) from the QSO sight-line, they found 

that there was a higher than average probability of an absorption feature, as is shown in 

Figure 2-11. This is the result of the cosmic web discussed in section 2.3 and shows that 

galaxies are formed in regions of large scale overdensity, as we would expect from 

simulations of the large-scale structure of the universe. 

At small separations (



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In a single MUSE field, we expect to detect 100 emitters over the redshift range 2.8 to 4 in a 

10 hour exposure. The typical sizes of the virial halos associated with these galaxies, as 

derived from the simulations, are 0.2 Mpc radius, only a factor 1.8 smaller than the Lymanbreak 

galaxies selected by Adelberger et al. Among these 100 galaxies, we expect to find 26 

objects bright enough to have spatially resolved information. There are 59 currently known 

QSOs at z>4 that are sufficiently bright (V>100, or 1% accuracy in absorption) can be obtained from the data cube. An optimal subsample 

can be selected from these quasars to ensure that the sight line is not blocked (at 

z>2.8) by a damped absorption line system. Re-observing the quasar with an echelle 

spectrograph like UVES would allow the absorption features to be deblended and centroided 

individually, but this is not strictly required since we will need only to determine the mean 

transmission at the systemic redshift of the proto-galaxy. A more significant issue is the offset 

between the systemic redshift and that measured from Lyman alpha emission, but Adelberger 

convincingly demonstrates that this can be determined by cross-correlating the absorption line 

data. 

If we targeting 4 such fields, we 

will obtain a sample of 400 objects, 

including 100 spatially resolved. In 

contrast Adelberger et al.'s results 

are based on only 6 objects. Thus, 

rather than simply detecting the 

neutral hydrogen deficit, the size of 

the MUSE sample will allow us to 

measure the strength of the superwind 

as a function of galaxies' 

emission line strength and 

continuum flux (both measures of 

the star formation rate). We would 

be able to divide the sample into a 

6x6 grid of luminosity and 

separation from the QSO sightline, 

and to still measure the average 

neutral hydrogen density in each 

bin to better than 5%. This sample 

will revolutionise our view of 

feedback, allowing us to study the 

strength, geometry and impact of 

the superwind as a function of the 

underlying star formation rate and 

galaxy mass. We have seen how 

the large galaxies samples 

available from the SLOAN survey 

have revolutionarised our view of 

the local universe. The size of the 

sample we derive from MUSE will 

similarly revolutionise our view of 

Fig 2-11. The mean transmission at the redshift of the protogalaxy 

as a function of the distance of the proto-galaxy from the 

line of sight, from Adelberger et al., (2003). The mean 

transmission at randomly chosen redshifts is shown as a 

horizontal dashed line. As the separation between the protogalaxy 

and the line of sight decreases, the transmission initially 

drops. This is due to the large-scale association of proto-galaxies 

and Ly α absorbers. This trend is expected from theory (as shown 

by the thin dotted and dashed lines). At small separations, 

however, the trend is reversed and the transmission increases. At 

separations



Issue: 1.3 

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the feedback in the high redshift universe. Although we will initiate this programme with the 

VIMOS spectrograph, only the throughput and greater field of view of MUSE will allow us to 

build the large and comprehensive database required. The equivalent programme would take 

40 times longer to complete with VIMOS due to MUSE's greater sensitivity and larger field 

of view (the programme cannot use the low dispersion mode of VIMOS). Only the higher 

spatial sampling of MUSE will allow us to spatially resolve the emission from the protogalaxies. 

In longer integrations we can identify much more distant emission-line galaxies. We will 

target QSOs at higher redshifts in order to determine how the strength and effectiveness of the 

super-wind feedback evolves with redshift. At present only three SLOAN QSOs (V30), but we will still 

be able to determine the mean absorption to better than 10% at the redshift of each emissionline 

object. In a single 80 hour exposure, we expected to detect almost 300 emission line 

objects which can be used for this study. 200 of these would lie at z>4, giving us an 

unparalleled insight into the role of feedback in the formation of the first galaxies and the 

widespread pollution of the universe with the first metals. 

References 

Adelberger, K.L., Steidel C.C., Shapley A.E., Pettini M., 2003, astro-ph/0210314 

Balogh M., Pierce F., Bower R.G. & Kay S., 2001, MNRAS, 326, 1228 

Benson A., Bower R.G. et al., 2003, ApJ, astro-ph/0302450 

Bower R.G., Morris S., Bacon R. et al. 2003, MNRAS, in press 

Cen R. & Ostriker J., 1993, ApJ, 417, 404 

Martin C., 1999, ApJ, 513, 156 

Navarro & Steinmetz 1997, ApJ, 478, 13 

Pettini M., Kellog E., Steidel C.C., et al., 1998, 508, 539 

Springel V. & Hernquist L., 2003, MNRAS, 339, 289 

Shapley A. E., Steidel C. C., Adelberger K.L., et al., 2001, ApJ, 562, 95 

Theuns T., Viel M., Kay S., et al., 2002, ApJ, 578, L5. 

White S. D.M., Rees M.J., 1978, MNRAS, 183, 341



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2.6. Ultra-deep survey using strong gravitational lensing 

The field size of MUSE is very well matched to those of strongly lensing clusters, which have 

typical Einstein radii of 15-30 arcsec, to a maximum of 45 arcsec for Abell 1689. The strong 

lensing of these clusters allows a number of unique studies, which are outlined below. 

2.6.1. Exploration of the luminosity function to much fainter limits 

The sizes of objects at our detection limit of 3.9 

10 -19 erg.s -1 .cm -2 are expected to be small, and 

the objects are essentially unresolved. Hence 

the S/N will be amplified by the magnification 

factor, which is between 3 and 5 over the whole 

field. Hence we can probe the luminosity 

function 3 times deeper, for an area which is 3 

times smaller than the typical deep field. We 

show a simulation in Figure 2-13. The lensing 

cluster has an Einstein radius of 30 arcsec, and 

the source distribution is taken from a GALICS 

simulation. Only the brightest Ly α emitters are 

shown in the image, where color indicates 

redshift. It is obvious that there are many 

multiply imaged galaxies, and many systems 

with large magnification. In total, over 200 

lensed objects are detected to our flux limit. 

The power of this technique was demonstrated 

by the discovery of a lensed Ly α emitter at 

z=6.56 by Hu et al (2002). This faint emitter has 

an apparent flux of 2.7 10 -17 erg.s -1 .cm -2 , and is 

lensed by a factor of ~4.5. Hence it would be 

undetectable without the lensing (which speeds 

up the integration time by a factor of 20 !). Ellis 

et al used lensing to find an even weaker source 

at z=5.6, with an unlensed flux of 

~3.10 -18 erg.s -1 .cm -2 , and a magnification of a 

factor of 30. Obviously, such strong lensing 

occurs only for a small area in the source plane 

and would not apply to most of the sources 

detected by MUSE, most of which are lensed by 

a factor between 2 - 5. 

Figure 2-12: An HST-ACS image of the lensing 

cluster Abell 1689 (Broadhurst et al, in 

preparation). This cluster is a prime candidate 

for strong lensing studies, given its very large 

Einstein radius, and the large number of arcs 

identified. Broadhurst et al identified at least 7 

systems which were multiply imaged. The upper 

panel shows the full image, the lower panels show 

some of the strongly lensed galaxies



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2.6.2. Constraints on the dark matter distribution and cosmology 

We expect roughly 10-30 multiply imaged sources in the datacube, in a wide range of 

redshifts. Since we know the redshifts of all sources, it is trivial to find the counter images, 

and unprecedented maps can be made of the mass distribution. The strength of the lensing 

signal as a function of source redshift depends on cosmology and the radial mass profile. If a 

sufficient number of multiply imaged sources are found, then the sources can be grouped into 

bins at the same radius and the cosmology dependence can be measured to great accuracy. 

2.6.3. Detailed studies of "large" arcs 

The Adaptive Optics capability of MUSE will allow, for the first time, to obtain very high 

resolution IFU spectroscopy of arcs in the optical. The simulation shows that 5-10 arcs with 

strong magnification can be observed per cluster. The MUSE observations will provide 

exquisite signal-to-noise and detail for these arcs. The AO-assisted spatial resolution of 

MUSE produces a gain of a factor of 3–4 in resolution compared to what other instruments 

can deliver in median seeing. The additional gain of the lensing is usually on the order of 5 

to 10 in the tangential direction, and 1–2 in the radial direction. The value of using arcs for 

high resolution studies has been demonstrated already. We show two examples in Figures 2- 

14 and 2-15. Figure 2-15 shows the spectrum of the arc at z=4.92 in the cluster MS1358+62 

(Franx et al 1997). This arc, the highest redshift arc known at this moment, shows clear 

velocity structure both in the very strong Ly α emission and interstellar absorption lines like Si 

II 1260 Å. The velocity offset 

between the lines was the first 

evidence for large-scale winds in 

high redshift galaxies. 

Another example in shown in Fig 

2-14, which shows a lensed z=1 

galaxy in the field of the cluster 

Abell 2218, observed by 

Swinbank et al (2003). The 

authors were able to reconstruct 

the 2D velocity field from the 

magnified O [II] 3727 Å 

emission-line field, and were 

able to derive an inclination 

corrected circular velocity which 

agrees well with the value 

expected from the (local) Tully- 

Fisher relation. MUSE will be 

able to extend this work to much 

smaller galaxies, and to 

continuum studies of high 

redshift galaxies. It will provide 

unique insight into the nature of 

high redshift galaxies which can 

otherwise only obtained by 30-m 

or larger telescopes. 

Figure 2-13: A simulation of an 80 hour MUSE observation of 

lensed Ly α emitters behind a cluster with an Einstein radius of 30 

arcsec. The original distribution of emitters was taken from the 

GALICS simulation. The color indicates redshift, with the reddest 

color at the highest redshift. As can be seen, many arcs are visible, 

with magnifications larger than 5. Furthermore, many multiply 

imaged sources can be identified, and sources well away from the 

Einstein radius are still significantly magnified.



Issue: 1.3 

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Figure 2-14: The structure of the z=1 lensed galaxy in the cluster Abell 2218. The left panel shows the 

HST image, the middle panel shows the flux distribution of the OII emission observed with GEMINI, with 

the observed velocity field superimposed. The right panel shows the velocity field after "de-lensing". It is 

very regular, and the derived rotational velocity agrees well with the Tully-Fisher relation. MUSE will 

allow these studies for arcs with much smaller sizes, lensed z=3 galaxies, etc. 

Figure 2-15: left: a long slit spectrum of the arc at z=4.92 in the cluster CL1358+62. The Ly α emission, and 

several interstellar absorption lines are clearly visible. These lines show structure along the arc, in flux and 

velocity. Right: the velocities of Ly α and Si II 1260 Å in the arc. Both lines show similar velocity structure, 

and a systematic offset between them. This structure is evidence for winds, where the neutral medium 

producing the Si II absorption line absorbs or scatters the Ly α emission at the same velocity. 

We plan to obtain 2 deep exposures with MUSE on a lensing cluster; but it is clear that 

MUSE will be the ideal instrument for the community to follow up lensing clusters. A prime 

candidate is Abell 1689, for which the HST-ACS image showed an unprecedented number of 

arcs (figure 2-13). We notice that lensing clusters are employed for deep searches in many 

wavelength bands, from sub-mm to optical, as they provide the unique opportunity to dig well 

below the standard sensitivity limits. MUSE will be the ideal follow-up instrument for most 

of these searches. 

References 

Ellis, R., Santos, R., Kneib, J.-P., Kuijken, K., ApJ 560, L119 

Franx, M., et al, 1997, ApJ 486, L75 

Hu et al, 2002, ApJ 568, L75 

Swinbank, A. M., et al, 2003, astro/ph 0307521



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2.7. Resolved spectroscopy at intermediate redshift 

The sensitivity and high resolution capabilities of 

MUSE will enable us to measure spatially resolved 

properties of galaxies at intermediate redshifts, z



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an emission line surface brightness through a Schmidt star-formation law, normalised to a 

total flux of 10 -17 erg s -1 cm -2 . Consistent with Fig 1, the velocity field can be mapped with 

MUSE to approximately two disk scale lengths. Clearly, in addition to the rotation curves for, 

e.g., Tully-Fisher studies as a function of internal galactic properties, any internal kinematic 

sub-structure on these scales will also be detected by MUSE, allowing for example the 

investigation of environment-induced perturbations to the velocity fields, and their possible 

effect on global galactic properties such as total galactic star formation rates and metal 

enrichment properties. 

For all galaxies with 0.25 10 -17 erg s -1 

cm -2 and R,I AB < 22.5. Note that this simple calculation does not take 

into account the beneficial effects of spatial inhomogeneities such as 

spiral structure in tracing the emission further out, nor does it take into 

account averaging over adjacent spaxels further out in the galaxy 

profile.



Issue: 1.3 

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study the radial gradients in a symmetric galaxy. The central panels of Figure 2-18 then 

show the resulting simulated S/N=15 spectra that are obtainable at redshift z=1, in a 80h 

MUSE DF integration. The spectra are obtained by combining simple stellar populations 

SEDs using the Bruzual & Charlot (2003) models. Finally, the right-most panels of Figure 2- 

18 show, for the two stellar populations, the 99% confidence levels for the recovered star 

formation histories in terms of average stellar ages and metallicities, with superimposed the 

correct average input values (red/blue stars). The epoch of the local star formation episode 

and the average metallicity of the stellar populations are both superbly recovered from the 

simulated MUSE data. These impressive capabilities of MUSE in probing the spatiallyresolved 

star formation histories of galaxies at intermediate redshifts will allow for 

quantitative tests of competing scenarios by discriminating between inside-out and outside-in 

formation and assembly of stellar mass in massive galaxies. 

Fig 2-18: Stellar population analysis achievable in a 80h MUSE integration at a galactocentric distance as 

given in Fig. 1 (but summing over a 9 pixels annulus) . Top and bottom panels describe two different star 

formation histories. Left: Modelled star-formation rates (solid lines) and calculated metallicities (dashed 

lines). Center: Corresponding simulated MUSE 15σ spectra. Right: Average stellar ages and metallicities 

recovered from the MUSE spectra (compared with input values, represented by the red/blue star). 

The number density of galaxies in the field with I AB 10 -17 erg s -1 cm -2 is of order 

6 per arcmin -2 and 2–3 arcmin -2 , respectively. Thus, the analysis of the several MUSE deep 

fields will already provide a useful sample of galaxies at those crucial intermediate epochs to 

be studied in full detail. Summing over a few adjacent spaxels inside galaxies will allow 

significantly fainter flux and surface brightness levels to be reached, while still disentangling



Issue: 1.3 

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radial variations in gaseous and stellar properties inside galaxies. A moderate binning over 

about 10 spaxels will about triple the number of galaxies per MUSE field for which spatiallyresolved 

spectroscopic information for the emitting gas is obtainable. The wider Medium 

Deep Field will also allow us to perform resolved spectroscopy of galaxies at intermediate 

redshifts. The three-fold higher limiting fluxes will reduce the number density of galaxies on 

which detailed data can be obtained by a factor of about three with respect to the Deep field, 

but this will be more than compensated by the factor of about ten larger area. The median 

redshift will be reduced by about 40%; however, it will still be in the realm where significant 

evolutionary changes are observed in the galaxy population. Unprecedented, the MUSE deep 

and medium deep fields will thus provide a sample of field galaxies in the z~0.5–1 redshift 

regime that will allow the investigation of the internal galactic properties as a function of 

fundamental global galactic properties such as mass and bulge-to-disk ratio. 

In addition to the observations of lensing clusters, which may be at relatively low redshift, 

studies of resolved galaxies in higher redshift galaxy clusters will certainly be developed by 

the scientific community. In such galaxy clusters at z ~ 0.8, the number density of galaxies 

with emission properties adequate for a spatially resolved study with MUSE is even higher 

than in the field. Using the K-band luminosity function of Ellis & Jones (2003), the typical I- 

K colors of cluster galaxies (Stanford et al. 2002), about 10–15 galaxies can be studied at the 

above levels with a single MUSE pointing in a rich cluster at z~0.8. At these intermediate 

epochs, theory and simulations predict major galactic transformations in clusters driven by 

environmental processes such as harassment and tidal stripping. The comparison of the 

spatially resolved stellar and gaseous diagnostics between intermediate-z galaxies in clusters 

and in the field will quantify the properties and timescales, and elucidate the physics, of such 

transformations. 

The study of the spatially resolved properties of normal galaxies will complement the 

exquisite study of a small number of highly-lensed objects behind clusters (section 2.6). 

Reference 

Bruzual, G. & Charlot, S., 2003, MNRAS, 344, 1000 

Carollo, C.M., Lilly, S., 2001, ApJL, 548, 153 

Chabrier 2003, PASP, astro-ph/0304382 

Ferreras, I. & Silk, J., 2003, MNRAS, 344, 455 

Lilly, S., Carollo, C.M., Stockton, A., 2003, ApJ 597, 730. 

Pagel, B., et al. 1979, MNRAS, 189, 95 

Stanford, S., et al, 2002, ApJS 142, 153 

van Zee, et al., 1998, AJ 116, 2805.



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2.8. Sunyaev-Zeldovich effect 

In the near future, systematic surveys for 

Sunyaev-Zeldovich (SZ) sources by 

bolometer arrays (e.g. BOLOCAM), 

interferometers and Planck and will produce 

large catalogues of clusters. Because the SZ 

effect is independent of distance, the 

selection is independent of redshift for a 

given mass and the redshift distribution is 

given by the cosmological volume element 

and the comoving density of clusters of the 

appropriate mass. for the concordance 

cosmology, BOLOCAM's dN/dz peaks at a 

redshift of z ~ 0.6 and falls by about a factor 

of 30 at z ~ 2. That of Planck peaks at even 

lower redshift z ~ 0.2 and falls by a factor of 

30 at z ~ 0.8 (Benson et al 2002). 

It is not possible to measure the redshifts of 

the density enhancements producing the SZ 

signal from the SZ effect itself. However, 

measuring the redshift distribution of the 

clusters would enable determination of 

important cosmological parameters. In 

addition to determinations of cosmological 

model Ω i which are independent from those 

derived from the CMB fluctuations, the 

accurately determined redshift distribution is 

sensitive to both the power spectrum 

normalisation, σ 8 , and the gaussianity of the 

primordial fluctuations (G) (see Benson et al 

2002). 

BOLOCAM has a beam of 1 arcminute 

which is perfectly matched to the FOV of 

MUSE. Planck's FWHM ~ 8 arcmin beam 

should still allow localisation of the 

gravitational potential within the MUSE 

FOV. At early epochs, it can no longer be 

assumed that cluster members can be 

identified from a tight "red sequence" of 

passively evolving elliptical-like galaxies. 

The unique capability of MUSE to 

determine redshifts for all galaxies within 

the FOV will make it the instrument of 

Fig 2-19. Redshift distribution of SZ sources from 

BOLOCAM and Planck (from Benson et al 2002) 

Fig 2-20: Variations in the N(z) for BOLOCAM SZ 

samples with gaussianity G (left) and σ 8 (right).



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choice to follow-up the most distant, and most interesting, SZ clusters, since the strongest 

peak in the redshift distribution will presumably be the redshift of the cluster. 

In fact, since these SZ-selected clusters are likely to be the most distant clusters selected only 

according to their mass (as opposed to hosting a powerful quasar or other atypical signpost), 

MUSE will be an ideal instrument to study the early evolution of rich environments which 

will evolve into the most massive clusters today. The physical size of the FOV (of order 500 

kpc at z ~ 2) is perfectly matched to the core radius of present-day clusters such as Coma. 

References 

Benson et al 2002, MNRAS 331, 71 

2.9. Late forming population III objects 

The transition between Pop III and Pop II (characterised by different star-formation processes, 

especially in the realm of cooling) is thought to occur at Z ~ 10 -4 Z sun . It is likely that the 

global enrichment of the Universe went through this transition at redshifts much higher than 

can be probed with MUSE (z ~ 15, or higher). Pop III objects may nevertheless be found 

forming at much lower redshifts, well within the MUSE-accessible range, if the metal 

enrichment from the earlier objects was not widely distributed through the IGM (see e.g. 

Scanapieco et al 2003), i.e. leaving 

essentially pristine regions in the voids. 

The fraction of Lyman α emitters that 

will be Pop III objects as a function of 

redshift is heavily dependent on the 

distribution of metals and fairly 

independent of the mean metallicity of 

the Universe or the precise value of the 

transition metallicity (Scanapieco et al 

2003). The luminosities of such objects 

depend on the poorly understood physics 

of young systems, such as the initial 

mass function. 

Recent studies with existing or new 

stellar tracks have predicted the 

properties of low metallicity and PopIII 

starbursts (Tumlinson et al 2001, 2003, 

Schaerer 2002, 2003). Interestingly it 

appears that proto-galaxies containing 

essentially PopIII stars could be easily 

distinguished from classical galaxies, 

due to the harder ionizing spectrum 

expected from metal-free stars, which 

strongly enhances the strength of He II 

recombination lines. 

Fig. 2-21 Predicted restframe EUV-optical spectrum 

of a young Population III galaxy with a Salpeter IMF 

from 1-500 Msun. The dashed line shows the pure 

stellar emission - the solid line the total emission. 

(Schaerer 2002)



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The following three features can be identified as clear signatures of very metal-poor or PopIII 

starbursts (primeval galaxies): 

• A larger Lyman continuum flux, 

and a continuum dominated by 

nebular emission, leading to a 

flatter intrinsic SED compared to 

normal galaxies. 

• For young bursts the maximum 

Ly α equivalent width increases 

strongly with decreasing 

metallicity from W(Ly α ) ~ 250-350 

Å at Z >~ 1/50 Z sun to 400-850 Å 

or higher at Z between 10 -5 and 0 

(Pop III) for the same Salpeter 

IMF. This is illustrated in Fig. 2-22 

considering also various IMF at 

low metallicity. 

• Strong HeII recombination lines 

(1640, 4686 Å) are a quite unique 

signature due to hot massive main 

sequence stars of PopIII/very low 

metallicity. Significant HeII 

emission is, however, only 

expected at metallicities below 10 -5 

solar (figure 2-21). 

Figure 2-22: Predicted Ly α equivalent widths for bursts of 

different metallicities and IMFs (from Schaerer 2003). 

The squares are for IMF 50-500Mo, the triangles 1- 

500Mo, and the circles 1-100 Mo. The various curves 

correspond to increasing metallicity from top to bottom. 

The second characteristic could also be 

found in AGNs, but enough spectral 

resolution (R>1000) will allow to 

distinguish between the two possibilities. 

With the help of the Table 4 from Schaerer 

(2003), the Ly α line emission can be 

estimated, for a constant star formation of 

SFR= 2 M sun .yr -1 , and a photon escape 

fraction of 0.5, with an assumed 

metallicity of 10 -3 solar (and a non 

extreme, intermediate, IMF), to be 4 10 42 

erg.s -1 , corresponding for a proto-galaxy at 

z=7, to the line flux of about 2 10 -17 

erg.s -1 .cm -2 . 

For the HeII 1640 line, the line luminosity 

in the same conditions is 10 39 erg/s, and 

will give a flux of about 10 -20 erg.s -1 .cm -2 , 

for a redshift z=5. This last line will be too 

faint to be detectable. However, at even 

Figure 2-23 : Predicted hardness as a function of 

metallicity for starbursts between PopIII and normal 

metallicities (from Schaerer 2003). The 3 curves are for 

the 3 different IMF indicated (Mup= 100 or 500Mo, 

Mlo=1 or 50Mo)



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lower metallicities, for truly primordial objects, the HeII 1640 line is favoured relative to Ly α 

(see Figure 2-21); for some assumed IMF, the HeII 1640 line luminosity could reach 4.10 41 

erg.s -1 at zero metallicity, corresponding to a flux of 3.10 -18 erg.s -1 .cm -2 , at z=5. Such a line 

would be easily detectable in the MUSE deep field. Finally, the MUSE ultra deep field 

described in section 6 (using gravitational amplification by a factor 3 in average), with its 

enhanced detection limit of 8.10 -20 erg.s -1 .cm -2 , should be able to detect simultaneously Ly α 

and HeII lines in the 2.8-4.7 redshift range for a metallicity lower than 10 -5 solar. 

Based on the models of Scanapieco et al (2003), the fraction of Ly α emitters that are Pop III 

objects could under certain scenarios be as high as 10% at z ~ 5 and L Lyα ~ 10 43 erg s -1 cm -2 . 

The point is that this is highly model dependent, and the detection of such objects (which is 

quite plausible) would greatly add to our understanding of the early chemical enrichment of 

the Universe. 

References 

Scanapieco, E., Schneider, R., Ferrara, A., astro-ph/0301628. 

Schaerer, D., 2002, A&A, 382, 28 

Schaerer, D., 2003, A&A, 397, 527 

Tumlinson, J., Giroux, M.L., Shull, J.M., 2001, ApJ, 550, L1 

Tumlinson, J., Shull, J. M., Venkatesan, A.: 2003, ApJ, 584, 608 

2.10. Active galactic nuclei at intermediate and high redshifts 

In a dramatic change of paradigm over the last years, active galactic nuclei (AGN) have 

altered their status from interesting but somewhat exotic objects into fundamental components 

of galaxy formation and evolution. This change was mainly triggered by the recognition that 

supermassive black hole (SMBHs) are ubiquitous in massive galaxies (Magorrian et al 1998). 

The striking near-equality between the local black hole mass density and the total density of 

matter accumulated through accretion in AGN (Yu & Tremaine 2002) suggests that periods of 

nuclear activity may in fact be common phases within galaxy evolution. An intricate link 

exists between black hole growth, the formation of galaxy bulges, and nuclear activity cycles; 

but most details are still missing from this picture. 

This is a challenge to theory and observers alike. In particular, the host galaxy and 

environmental properties of AGN at redshifts around and beyond z~1 will allow one to set 

strong constraints on formation scenarios. This is an area where MUSE will provide 

significant progress, because of its capability to reach very faint flux levels at good spectral 

resolution, combined with a high multiplex factor over an astrophysically relevant field size 

of ~ 250 kpc. A fundamental advantage of MUSE over existing or other planned instruments 

is the integration of the traditional multi-stepped approach of imaging, low-resolution and 

high-resolution spectroscopy into a single observation.

Assuming that most of the black hole mass of 

present-day galaxies was assembled around 

the period of maximum AGN space densities, 

between z ~ 1 and z ~ 3 (Wolf et al 2003), the 

task is to establish an evolutionary link 

between AGN at high z and the local galaxy 

population. Such a link can be built by 

studying the environments of AGN and 

characterising the degree of overdensities they 

live in. Luminous radio-loud quasars and radio 

galaxies beyond z ~ 1 are typically located in 

rather rich structures, probably progenitors of 

the most massive clusters today 

[REFERENCE]. The environment of lower 

luminosity radio-quiet AGN at high z, on the 

other hand, is not well constrained. A set of 

MUSE pointings on a representative AGN 

sample, of a few hours exposure time each, 

would yield a complete census of the 100-200 

kpc surroundings down to significant sub-L* 

luminosities at z=1 and to roughly L* at z=3. 

At the same time one would get the velocity 

information needed to assess the degree of 

virialisation in a given structure. 



Issue: 1.3 

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Figure 2-24: This 1 arcmin x 1 arcmin section of 

an HST image in the Chandra Deep Field South 

contains 7 X-ray sources, most of which are likely 

AGN at intermediate to high redshifts. Some of 

these sources were already targeted 

spectroscopically with the VLT, but are optically 

too faint to give a meaningful spectrum. A single 

deep MUSE exposure would not only clarify the 

nature of these, but at the same time allow to 

study their host galaxies and environments. Image 

taken from the GEMS project. 

Gravitational interaction and merging are believed by many to be the main drivers for driving 

nuclear activity. But what exactly are the conditions needed to trigger an AGN? Until today, 

the search for morphological clues has largely prevailed, but spectroscopic diagnostics can 

deliver additional, possibly crucial pieces of evidence. A single MUSE data cube could reveal 

also, for example, large-scale gas streamers, patterns of enhanced star formation in the AGN 

host as well as in other galaxies in the field, and kinematical signatures of recent merger 

events, thus provide essential clues about the physical drivers of cosmic AGN evolution. 

Most of the sketched AGN studies would have to be performed in pointed mode, targeting 

individual pre-selected objects. The "blind" MUSE surveys outlined elsewhere in this 

document provide a chance to integrate the AGN aspect into a multi-purpose survey, by 

judiciously selecting survey fields to coincide with deep X-ray pointings. Recent surveys with 

Chandra and XMM have yielded surface densities of more than 3 X-ray sources per arcmin2, 

a large fraction of which is presumably directly linked to some sort of AGN phenomenon. 

These surveys are still largely photon-limited, and even deeper pointings are being considered 

which would increase the surface density by at least another factor of 2 (Alexander et al 

2003). The suggested concepts of "shallow" and "medium deep" MUSE surveys could in fact 

revolutionise the traditional strategy of X-ray imaging/spectroscopic follow up. In particular, 

spatially resolved information for every single X-ray AGN would become available at once, 

allowing to deblend the nuclear from the host galaxy spectrum and obtain a much cleaner 

spectral diagnostic. At the same time, kinematics and environmental information would 

become available, with all the benefits mentioned.



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References 

Alexander, D.M., et al, 2003, AJ 126, 539-574 

Magorrian, et al, 1998, ApJ 115, 2285-2305 

Wolf, C., Wisotzki, et al., 2003, A&A 408, 499-514 

Yu, Q., Tremaine, S., 2002, MNRAS, 335, 965-976 

2.11. The development of dark matter haloes 

The fundamental prediction of hierarchical models of structure formation in the Universe, 

such as the standard ΛCDM model is that the virialised mass of haloes should grow with time 

through the accretion of smaller halos. The prediction from Press-Schechter that the mass 

function of virialised structures evolves by increasing the characteristic mass M* while 

decreasing the low mass end amplitude is born out by numerical simulations of the dark 

matter distribution. 

Determining masses of haloes at high redshift is non-trivial. On cluster masses, where the 

redshift evolution is at the present epoch strongest (above M* in Press-Schechter) there is a 

great sensitivity to cosmological parameters (see SZ section 8) in the comoving density of 

bound objects of a given mass as a function of redshift. On galactic masses, around and 

below M*, there is a smaller dependence on redshift. 

As an integral field spectrograph, MUSE will automatically produce 2-dimensional velocity 

fields for all emission line galaxies (and brighter absorption line galaxies) in the field of view 

(see Section 2.7). A potentially unique capability of MUSE (at significant redshifts) is to 

relate the inner and outer halo kinematics at high redshifts, through the velocity dispersion of 

satellites 100-250 kpc from isolated massive galaxies (Zaritsky and White 1994, Prada et al 

2003). MUSE can be used to identify and measure accurate velocities of all star-forming 

satellites around high redshift galaxies. Even a one hour exposure is sufficient to detect at 5σ 

a compact (0.8×0.8 arcsec 2 ) line emitting galaxy with a line flux of 4.1×10 -18 erg s -1 cm -2 , 

equivalent to a line luminosity at z ~ 1 of about 2 ×10 40 erg s -1 or a star-formation rate of 

about 0.3 M sun yr -1 (Kennicutt 1992). 

The goal of this analysis would be to determine M halo (L galaxy ) at z ~ 1, a regime beyond the 

range where lensing studies have much leverage (because of the background redshift 

distribution). This measurement is automatically combined with measurements of the size 

and the rotation curve of the luminous component of the host galaxies. 

This science area comes for free in all of the survey observations, e.g. the 200 fields of the SF 

survey. Additionally, deep redshift surveys, such as VIMOS or COSMOS, could be used to 

construct very well defined samples of galaxies, e.g. "isolated" galaxies for which this sort of 

study is best performed (e.g. Prada et al 2003), for observations by the community in GO 

mode. 

References 

Zaritsky and White 1994, ApJ 435, 599 

Prada et al 2003, ApJ 598, 260



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2.12. Merger rate 

In the hierachical picture of galaxy formation and evolution, galaxies as seen today are the 

end product of a long list of merger events. The merger tree as seen in simulations can be 

quite complex, and, given a typical merger timescale of several hundred million years, a 

galaxy will witness along its life a few major merger when units of similar size / mass 

interact, and many more minor mergers with smaller units. As we go back in time, we expect 

to witness more of the merger events, as the number of elementary building blocks is 

predicted to be larger than today. While we have many examples of galaxy mergers in the 

nearby universe, the evolution of the merger rate as a function of look-back time, hence our 

understanding of how galaxies build up with time, is still poorly constrained. Current 

measurements indicate that the merger rate evolves as (1+z) m , with m=2.5-4 out to z~1. To 

better constrain this measurement it is necessary to obtain for a large sample of galaxies 

representative of the general galaxy population accurate relative velocity information of 

galaxy companions to predict whether a merger is probable or simply a chance projection. At 

redshifts 1-3, measuring the environment of bright galaxies with a velocity accuracy of 10-30 

km/s, down to 3 magnitude below M* will allow to map the growing of several hundred 

galaxies and strongly constrain the merger rate. 

The MUSE Deep Fields will allow measurement of the major merger rate from all interacting 

galaxies with a similar mass out to the faintest galaxies in the survey. This should allow to 

derive the merger rate with an accuracy of ~10%. Furthermore, as described in section 2.10, it 

will be possible to measure the rate of minor mergers from the small satellites around a well 

defined sample of galaxies out to a redshift ~1.



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2.13. Survey strategy 

The majority of the science goals described in the previous sections can be addressed with the 

following staggered survey: 

• Shallow field (SF) covering a larger sky area (200 arcmin²) with an 1 hour integration 

time by exposure. Note that this survey does not need AO capabilities. 

• Medium deep field (MDF) covering a relatively large sky area (40 arcmin²) at 

improved depth 

• A few deep fields (DF) at random location covering a small area (3 arcmin²) but at 

extreme depth (3.9 10 -19 erg.s -1 .cm -2 ). 

• Ultra deep field (UDF) using strong lensing to improve the detection limit by a factor 

3 or more. 

Integ. 

Tot. Total Limiting mag. I AB 

Field 

time by Nb of Area integ. 

Limiting Science 

Id. Location exp (h) field arcmin² time (h) Full R R/20 flux F subjects 

Not yet 

SF specified 1 200 200 200 22.2 23.9 50 6 

MDF 

Random 

& QSO 10 40 40 400 23.9 25.5 11 

1,3,5,7,9,11, 

13,15,17 

DF Random 80 3 3 240 25.0 26.7 3.9 

1,3,5,7,9,11, 

13,15,17 

UDF 

Lens 

cluster 80 2 0.6 160 26.2 27.9 1.3 2,8,13,15 

Limiting flux is in 10 -19 erg.s -1 .cm -2 units.



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Science area 

Assembly 

galaxies 

Assembly 

galaxies 

Assembly 

galaxies 

Assembly 

galaxies 

Assembly 

galaxies 

Assembly 

galaxies 

Intergalactic 

medium 

Intergalactic 

medium 

Intergalactic 

medium 

of 

of 

of 

of 

of 

of 

Observations 

Determination of Ly α luminosity 

function and correlation function at 

z=[2.8-6.7] 


function and correlation function at 

z=[2.8-6.7] 

Merger rate 


function (faint end) and correlation 

function at z=[2.8-6.7] 

Determination of Ly α (very faint 

end) luminosity function at z=[2.8- 

6.7] 

Correlation function of Ly α emitters 

at z=[2.8-6.7] 

Development of dark matter halo 

using velocity dispersion of 

satellite galaxies at z~1 

Detection of the cosmic web using 

Ly α emitters at z=[2.8-6.7] 


extended Ly α halos at z=[2.8-3.5] 


fluorescent emission 

Field 

Id. 

SF 

MDF 

DF 

UDF 

Obj. by field 

Nb fields Total 

objects 

2.8



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Fig. 2-25: Simulated MUSE deep field from GalIcs simulation. Galaxies are coloured 

according to their apparent redshift. Galaxies detected by their continuum (I AB < 26.7 ) 

and/or by their Ly α emission (Flux > 3.9 10 -19 erg.s -1 .cm -2 ) are shown. 

For surveys, as opposed to studies of previously identified objects, the power of an integral 

field spectrograph relative to a multi-slit spectrograph observing previously identified (i.e. 

continuum-selected) objects, is to survey "blank fields". Especially for objects with strong 

emission lines, such as expected for young star-forming galaxies, a survey for emission lines 

with an integral field spectrograph reaches to extremely faint continuum levels. An emission 

line galaxy with line flux 3.9×10 -19 erg s -1 cm -2 at 9200 Å and an equivalent width of 200 Å 

(as seen in a good fraction of emission line objects at this wavelength, from Hα at z ~ 0.4, 

[OII] 3727 at z ~ 1.4 and Lyman α at z ~ 6.5) has a continuum Z AB ~ 30.0, far below the point 

where systematic spectroscopy of continuum-selected galaxies is feasible or attractive 

(because most such galaxies would not have detectable lines). 

Thus the integral field approach is most attractive (relative to the others) at faint levels and a 

MUSE survey should seek to survey down to the faintest possible levels, i.e. well below the 

current depth of narrow band surveys (~10 -17 erg s -1 cm -2 ). One consequence of this is that 

complementary data to the MUSE data cubes (e.g. imaging and photometry outside of the 

MUSE wavelength interval or very high resolution imaging within it) must be 

correspondingly deep (AB ~ 30 magnitude) if at all possible.



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The main issue for a MUSE survey is the limited field of view (1 arcmin 2 ). The comoving 

scale corresponding to 1 arcmin does not change strongly with redshift at z > 2 and is 

approximately 2.5 Mpc. For studying the assembly of individual galaxies, surveying scales of 

only a few Mpc is sufficient. At turn-around, the 6×10 10 M 

of matter which is currently 

within the virialized halo of a ~L* galaxy such as the Milky Way is contained within a 

volume of radius 750 kpc. Thus a survey field of side 2.5 Mpc should contain all the baryonic 

material that will assemble into individual L* galaxies. 

The other physical scale of interest is the clustering scale of galaxies, about 5-10 comoving 

Mpc. This enters into the issue of sampling variance since it means that the galaxy population 

within spheres of this size is highly correlated and the statistics of galaxies are far more noisy 

than simple consideration of their numbers would indicate - put another way, the n galaxies 

within a sample do not represent n statistically independent entities but rather n/m entities, 

where m may be calculated from the correlation function knowing the survey geometry. 

Surveys on arc-minute scales are dominated by this sampling variance: a good example is the 

very different population of red galaxies in the HDF-N and HDF-S which, on their own would 

lead to quite different interpretations of the global star-formation rate. 

Given the limited field of view of MUSE, the two strategies for overcoming sampling 

variance are (a) to observe adjacent contiguous fields to build up a larger area, or (b) to 

observe widely separated fields. Of these, 

the second is much more efficient: the 

statistical weight of the survey builds up as 

N 0.5 (where N is the number of MUSE 

pointings) whereas in (a) the gain is more 

like N 0.3 . Thus the optimum survey 

strategy would be to observe multiple 

widely spaced pointings. 

A good feature of this is that this strategy 

naturally accommodates the need for m ~ 

17–18 guide stars for the AO system. 

Unfortunately, at present, most of the deep 

extragalactic survey fields, and especially 

those that have been observed with the 

HST) consist of a handful of large 

contiguous areas (e.g. GOODS-S 10×16 

arcmin 2 ). However, within these, multiple 

pointings around available stars could be 

made (Fig. 2-26). 

Figure 2-26: Potential guide-stars in the CDFS 

region, each surrounded by a 90 arcsec radius 

region. Axes are decimal degrees in RA and dec



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2.14. A pan-chromatic view of galaxy formation 

In this section we describe complementary surveys that should be carried out within the same 

volume of space as the MUSE deep fields in order to extract the maximum possible science 

from the data. We show that by observing for comparable time periods with ALMA, e-VLA, 

JWST, and possibly SKA, one can map out the neutral and molecular gas content of the 

volume to compare with the ionized gas seen in the MUSE and JWST data, and the starlight 

imaging information from JWST. 

2.14.1. JWST 

Thanks to its unrivalled sensitivity in the thermal infrared JWST is designed to be a major 

player in the understanding of formation and evolution of galaxies. JWST has a wide spectral 

range (0.6-28 µm), but it is only above 1 µm, and probably even above 2 µm, that it shows its 

strength with respect to ground based telescopes of similar or larger aperture. Among the 3 

instruments, the ESA NIRSPEC spectrograph is particularly well suited to distant galaxy 

study because of its multiplex capabilities in the 1-5 µm wavelength range. NIRSPEC is able 

to observe simultaneously a maximum of 100 objects in a 3x3 arcmin², at two spectral 

resolutions 6 of 100 and 1000. The 2.9-5 µm grating of the R~1000 mode is of special interest 

given its almost perfect match in H α redshift range (3.5-6.7) with MUSE Ly α coverage (2.8- 

6.7). According to simulations (section 2.2) a MUSE deep field should give 150-200 Ly α 

emitters in that redshift range. Depending on the source clustering, in two to four exposures of 

15 hours each, NIRSPEC would be able to detect the H α line of all MUSE high z objects 7 

with a comparable S/N. Having access to Ly α and H α would not only confirm unambiguously 

the redshift of the source, but will give access to dust extinction and star formation rate 

measurements. Comparison of line profiles would also tell us about resonant scattering and 

velocity shifts of Ly α . Moreover this pre-selection of sources by MUSE should be at least as 

efficient as multi-band deep imaging with NIRCAM. The combination of VLT/MUSE and 

JWST/NIRSPEC is quite attractive and should strengthen the JWST European scientific 

return. 

2.14.2. ALMA 

The Atacama Large Millimetre Array (ALMA) will provide the principal means of measuring 

the rest-frame FIR emission of galaxies as an observed submm/mm continuum, and their 

molecular gas content via the CO lines. 

With its 64 antennas (each of 12 m diameter), its 16 GHz bandwidth and its first four 

receivers covering the 86–116, 211–275, 275–370, and 602–720 GHz bands (before 

completion to a final figure with 10 bands), ALMA will reach at least 100 µJy (5 σ) in an 

6 NIRSPEC has another mode with a higher spectral resolution (R~3000) but without multiplex capabilities (only 

one single long slit). 

7 For the 3.9 10 -19 erg.s -1 .cm -2 MUSE detection limit is able to detect a Ly α line of 2.7 10 -18 erg.s -1 .cm -2 with 85% 

obscuration. This translates into 3.1 10 -19 erg.s -1 .cm -2 H α flux, assuming a flux ratio of 8.7 between the two lines.



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hour. It will require 11 (resp. 5, 2) pointings to cover a 1 arcmin 2 field at 350 (resp. 230, 140) 

Ghz, and will detect several hundred galaxies arcmin -2 in the continuum and in several 

transitions given an hour per pointing (Blain et al. 2000). Since the observation of the Cosmic 

Infrared Background (Puget et al. 1996), the detection of a high number of submm sources by 

the ISOPHOT and SCUBA instruments, and the discovery of a high UV extinction in Lybreak 

galaxies (e.g. Adelberger and Steidel 2000), there has been a growing awareness of the 

necessity to study galaxy properties both at optical and IR/submm wavelengths. The energy 

emitted by young stars heats up dust and is released at rest-frame IR wavelengths. Thus the 

thorough study of the SFR in galaxies requires an accurate assessment of the luminosity 

budget. The current and forthcoming observations are confusion limited (SCUBA, 

SIRTF/MIPS, Herschel/SPIRE, Planck/HFI) and the detected objects at high redshifts are/will 

be the so-called LIRGs and ULIRGs, that is, rather extreme objects (with a density of about 1 

arcmin -2 at the 2 mJy level at 350 GHz). Only ALMA will be able to have access to the 

emission of normal high-redshift galaxies. The GalICS model used in section 2 predicts that 

the median flux of MUSE Deep Survey sources at 350 Ghz is 21 µJy. About 25 % of MUSE 

Deep Survey sources will be detectable with a dedicated survey of 100 hours per arcmin 2 

(typically 10 pointings of 10 h) that reaches 40 µJy (5 σ), with a spatial resolution of 0.2 

arcsec, comparable to the one of MUSE. It is interesting to note that, within the assumptions 

of the model, almost all the ALMA sources at 2.8



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minimum depth of 5x10-23 Wm-2 (5 sigma), sufficient to detect tens of galaxies with M(H2) 

~ 1x10 11 M sun , hundreds of galaxies if line ratios typical of low-excitation conditions prove 

common (Blain et al. 2000; Papadopoulos et al. 2001; Papadopoulos & Ivison 2002). 

At a 0.3 x L* mass limit, SKA is anticipated to detect 10-20 galaxies in a 1x1' field (actually a 

tiny fraction of its field of view) between z=2.5-3.5, several more at an L* mass limit between 

z=3.5-4.5 (as well as several hundred between z=1.0-2.5 at far lower mass limits). 

The selection of objects in the field would be entirely based on HI, not on the associated 

stellar component, and is therefore independent of the effects of extinction, colour and optical 

surface brightness. The combination of deep, HI-selected samples and deep, optically-selected 

samples will be extremely powerful for studying galaxy evolution over a large range of 

redshift. 

In addition to HI content, such a survey would also measure the long wavelength radio 

continuum emission of the galaxies in the field, which is known to be an excellent indicator of 

the massive star-formation rate, independent of the effects of extinction. This information can 

be used to link the star-formation rates to the HI content of galaxies as a function of redshift 

and environment. It will also provide an independent estimate of the evolution of the 

comoving star-formation-rate density to be compared with the optically determined functions.



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3. Nearby galaxies 


The various studies of high-redshift objects will yield a wealth of information concerning the 

formation and evolution galaxies. However, the faintness and extremely large distances of 

these objects generally prevent detailed studies of the underlying physical processes. For this 

reason, interpreting high-redshift observations through the use of simulations and models 

relies heavily on the results of work conducted on nearby galaxies. Only by studying and 

understanding the local universe will it be possible to determine the complex physics that 

shapes the universe at very early epochs. 

MUSE will make significant contributions to our understanding of nearby galaxies, making 

use of both the high-resolution mode, to resolve the complex structures of galaxy nuclei, 

measure black-hole masses, and perform crowded-field spectrophotometry in local objects; 

and also of the wide-field mode, allowing sub-kiloparsec scales to be accurately resolved at 

distances beyond 100 Mpc, whilst simultaneously providing a global view of entire systems: 

ideal for relating nuclear properties of galaxies to their outer parts, or accurately mapping 

multi-scale phenomena such as galaxy mergers. The large spectral domain of MUSE also 

makes it a uniquely versatile instrument, which will herald progress in a diverse range of 

science topics in the nearby universe, from stellar dynamics and population studies, to 

complex 'gastrophysics' and the properties of AGN. 

MUSE will allow quantification of some of the most fundamental processes of astronomy, 

which have so far eluded a proper understanding from currently available data. For example, 

probing the environment of black holes, as well as determining accurately their physical 

properties, will help explain the nature of these phenomena in the global context of galaxy 

formation. Connecting stellar dynamics and stellar populations directly with morphological 

structure will reveal the true fossil evidence contained in nearby galaxies. Mapping 

interacting galaxies on various scales will quantify the impact of merging on galaxy 

evolution. And detailed study of star-formation and galactic winds will shed new light on the 

question of feedback mechanisms in galaxy formation. 

3.2. Supermassive black holes in nearby galaxies 

In recent years there has been tremendous progress, primarily from space-based observations, 

in our understanding of the distribution of BH masses and the relation between the BHs and 

their host galaxies. From such observations, a picture of BH demography has emerged, 

summarized by the correlation between BH mass and absolute spheroid luminosity (e.g., 

Kormendy & Richstone 1995; Magorrian et al. 1998) and the much tighter correlation 

between BH mass and galaxy central velocity dispersion σ (Ferrarese & Merritt 2000; 

Gebhardt et al. 2000). It is now clear that BHs are nearly ubiquitous in galaxies with bulges, 

and that their evolution is intimately linked to the evolution of the host spheroid. BH studies



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are now shifting towards higher redshift, generally assuming that the above correlation also 

applies in the early universe. 

The need to probe the BH radius of influence, inside of which its gravity dominates the stellar 

motions, demands sub-arcsecond spatial resolution, even for nearby galaxies. Assuming the 

BH-σ relation is valid for all galaxies, a typical object with central velocity dispersion of 

σ~200 km s -1 is expected to contain a BH of mass M BH ~10 8 M ☼ , and this will significantly 

affect the galaxy kinematics up to a radius R~0.2”, when observed at the distance of the Virgo 

cluster. For this reason most BH masses cannot be reliably measured with ground-based 

seeing, and STIS onboard HST has been used for the most well-determined BH masses 

measured from dynamical modeling of stars or gas within the BH radius of influence. 

STIS has some critical limitations, however. Kinematics are only obtained along a single slit, 

generally placed across the galaxy centre, and aligned with the photometric position angle. As 

a result of this, there is an intrinsic uncertainty in the true orientation of the gas and stellar 

kinematic axes. To illustrate this, Figure 1 shows the predicted velocity field for a thin disk of 

gas orbiting in the combined potential of the stellar density distribution of NGC4660, 

computed by deprojecting HST/WFPC2 photometry, and a central supermassive BH of mass 

M BH ~10 8 M ☼ as expected from the BH-σ relation for this galaxy. It is apparent from the 

figure how much a small uncertainty in the position of the slit, with respect to the disk 

kinematical axes, can affect the observed kinematics. 

Moreover a single slit observation is not 

sufficient to detect possible signs of nonaxisymmetry 

in the stellar density 

distribution, or of non-circular motion in 

the gas velocity field. This means that the 

symmetry assumptions generally made in 

the dynamical models, used to measure the 

BH masses, cannot be tested with long slit 

data. Multiple slit observations could in 

principle be used, to cover a continuous 

2D field, but this would lead to 

unreasonably long integration times with 

the already relatively small 2.4m HST 

mirror. Moreover STIS is an ageing 

instrument and HST may not be available 

much longer. 

Another reason for the need of high 

resolution integral-field data for the 

determination of BH masses comes from 

Figure 3-1: predicted gas velocity field for a thin 

disk, inclined by i=50°, orbiting the combined 

potential of NGC4660, and a central supermassive 

BH of mass of 10 8 M ☼ 

, as in Figure 1. The 0.1” 

HST/STIS slit is overplotted for comparison. Note 

that a very small uncertainty in the positioning of 

the slit can dramatically affect the derived gas 

kinematics. 

simple dimensionality arguments, which suggests that the stellar orbital distribution (e.g. the 

anisotropy) cannot be recovered without the knowledge of the line-of-sight velocity 

distribution of the stars at all spatial positions on the projected galaxy image on the sky. 

Ignorance on the anisotropy directly translates into large uncertainties in the BH mass 

determination (e.g. Verolme et al. 2002).



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Figure 3-2: Expected stellar mean velocity (left panel) and velocity dispersion σ (right 

panel), for a MUSE observation of the elliptical galaxy NGC4660, assuming the galaxy 

contains a supermassive BH of 10 8 M ☼ 

as predicted by the BH-σ relation. The 

kinematics was computed by fitting a dynamical model to integral-field SAURON 

kinematics and HST photometry. Smoothness was enforced in the intrinsic orbital 

distribution to constrain the model at the MUSE higher spatial resolution. Note the 

characteristics decrease of σ along the major axis, due to the fast rotating nuclear 

stellar disk. A typical galaxy isophote is overplotted with the ellipse. 

To simulate the expected quality of the kinematical data obtained with MUSE we generated a 

realistic dynamical model for the elliptical galaxy NGC4660, with an assumed BH mass as 

predicted by the BH-σ relation. The kinematics of the model was then observed at the MUSE 

highest sampling of 0.025” per spatial element, properly convolved with a simulated NFM (4 

laser guide stars) PSF as obtained at 0.93 µm (Figs 3-2 and 3-3). Detailed calculations show 

that, with a 10 hours exposure, MUSE can reach a S/N~30 per spectral resolution element 

down to a surface brightness of 13.5 mag arcsec -2 in the I-band. At this central surface 

brightness the nuclei of most 

nearby early-type galaxies 

can be observed. Taking into 

account the large number of 

spectral pixels sampled by 

MUSE this S/N will allow 

the extraction of the line-ofsight 

velocity distribution 

with an error



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significantly better spatial sampling, (iii) the ability to measure the velocity anisotropy, and 

(iv) much shorter exposure times, due to its much higher throughput and the order of 

magnitude increase of the mirror size of VLT compared to HST. This superiority over STIS 

will make it possible for the first time to measure accurate BH masses even in giant ellipticals 

with extended low-surface brightness cores. 

The performance of MUSE for the study of BHs in galaxies is comparable to what can be 

obtained with SINFONI, using the SPIFFI integral-field mode in the K band (2.2 µm) and a 

natural guide star AO. Similar results should be expected also with the laser guide star mode. 

This is as expected, since both instruments have high throughput and would be on the same 

8.2m telescope. Our simulations show that the sharper core of the MUSE PSF is somewhat 

compensated by a smaller halo in the SINFONI PSF, according to the current PSF estimates. 

MUSE however allows observations over a larger spectral range and can detect important 

absorption and emission features at optical wavelengths. 

References 

Kormendy & Richstone 1995, ARA&A, 33, 581 

Magorrian et al. 1998, AJ, 115, 2285 

Ferrarese & Merritt 2000, ApJ, 539, 9 

Gebhardt et al. 2000, ApJ, 539, 13 

Verolme et al. 2002, MNRAS, 335, 517



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3.3. Kinematics and stellar populations 

Early-type galaxies are thought to have formed when the universe was still in its infancy, and 

hence provide a wealth of fossil information from the evolutionary processes that have shaped 

the universe we observe today. Key components of this fossil record are the kinematics of the 

system, both of the stars and any gas that may be present; and the distribution of stellar 

populations, in terms of their age and chemical composition. 

There is observational evidence that early-type galaxies are strongly influenced by recent and 

ongoing evolutionary mechanisms. Many galaxies are found to contain kinematically 

decoupled components (KDCs), where a significant portion of the galaxy has distinct 

dynamical properties from the rest of the galaxy. Moreover, many early-type galaxies exhibit 

components with distinct chemical properties, showing a different age and/or metallicity 

distribution from the rest of the galaxy. Figure 3-4 illustrates these structures using results 

from the SAURON survey (Bacon et al. 2001, de Zeeuw et al. 2002), showing the diverse 

kinematic and chemical components which exist in many early-type galaxies, and which are 

clearly revealed with integral field spectroscopy. The existence of such substructure within 

these objects suggests that early-type galaxies are formed hierarchically, through the merging 

of smaller systems (e.g. Baugh 1996). 

Figure 3-4. Selection of early-type galaxies observed with SAURON. Top row shows the reconstructed 

images, which are regular and smooth. The middle row shows the velocity field, and the bottom row shows 

the distribution of Mg b absorption strength. Galaxies with strong rotation also exhibit a flattened Mg b 

distribution, which is absent in the galaxies with different kinematics. This shows the variety of structures 

found in early-type galaxies, and the strong connection between kinematic and chemical galaxy properties. 

Models of hierarchical galaxy formation also make strong predictions about the influence of 

environment on galaxy evolution. In the deep potential-well of rich clusters, the dense intragalactic 

medium strips the reservoir of star-forming material from galaxies, and the high 

random velocities of the constituent galaxies inhibits merger events. Galaxies within such 

clusters experience their last merging events at high redshift (z ≥ 2), and have since evolved



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quiescently, resulting in old stellar populations. Alternatively in low-density environments, 

galaxies can accrete material and experience major merging events at very low redshifts (z



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Figure 3-5 illustrates this, comparing kinematic observations of a galaxy at a distance of 100 

Mpc with the equivalent measurements possible with VIMOS. Figure 3-5 (a) shows firstly the 

dramatic increase in spatial coverage provided by MUSE. For clusters at distances greater 

than 100 Mpc, it will often be possible to survey multiple galaxies in a single field, greatly 

increasing the surveying efficiency. Figure 3-5 (b) shows a sub-region of the MUSE field, 

containing an input model velocity field of a typical elliptical galaxy at 100 Mpc, overlaid 

with isophotes of constant surface brightness. This shows a counter-rotating core with a 

velocity amplitude of 60 kms -1 , and a physical size of around 100 kpc, typical of decoupled 

cores found in very nearby galaxies. Figure 3-5 (c) shows the same field as observed by 

MUSE in the wide-field mode. These observations are based on a 2-hour integration in the I- 

band, using the Calcium II triplet region to determine the kinematics. The data have been 

spatially binned using the optimal Voronoi tesselation method of Cappellari & Copin (2003) 

to obtain a minimum signal-to-noise ratio (S/N) of 30 per spectral resolution element. Figure 

3-5 (d) shows the same input field, as it would be observed with VIMOS, using a similar 

spatial and spectral sampling. The superior spatial sampling of MUSE is clearly demonstrated 

by the ability to resolve the decoupled core. 

The extensive wavelength coverage of MUSE, coupled with the relatively high spectral 

resolution, will also allow accurate modelling of stellar populations. Several studies have 

shown that even dynamically evolved systems like elliptical galaxies can have a significant 

spread in the total luminosity-weighted age of their stellar populations (Worthey 1994, Trager 

2000). The young ages obtained for these evolved systems can be explained by a 'frosting' of 

younger stars that contribute a significant amount to the total integrated light, while 

constituting only a small fraction to the mass of the total system. In this way, studies based on 

the classical line-strength indices, such as the Balmer lines and metal features at visible 

wavelengths, are strongly biased towards any young populations that may be present. MUSE, 

however, provides continuous coverage from the visible region into the near-infrared, 

including in a single exposure many key features for stellar population diagnostics. These 

features include the major Balmer lines: crucial for measuring young stars (in absorption) and 

star formation (in emission); so-called 'α-elements', such as Magnesium and Oxygen, 

necessary for determining stellar 

abundances and the enrichment 

history of type II super-novae; 

the near-infrared Calcium II 

triplet: a sensitive measure of the 

IMF; as well as numerous Iron 

absorption features for 

determining metallicity. 

The power of combining these 

diagnostics becomes most 

apparent when trying to separate 

two superimposed populations. 

Figure 3-6 illustrates the simple 

example of a young (25 Myr), 

fast-rotating stellar disk 

embedded in an old (11 Gyr) 

Figure 3-6. Weighted combination (green line) of a 

young (25 Myr: blue line) and old (11 Gyr: red line) SSP 

model spectra (Bruzual & Charlot 2003). The young 

population contributes around 1% of the mass along the 

line of sight.



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non-rotating, pressure-supported spheroidal system. The contribution from the young disk is 

weighted such that it contributes only around 1% of the mass along a given line-of-sight, and 

is combined with the underlying old population, to give the spectrum given in Fig. 3-6. This 

shows that, although the young population dominates at blue wavelengths, the old population 

gives the most significant contribution in the near-infrared. 

(a) 

(b) 

Figure 3-7. (a) Rotation velocity (top) and velocity dispersion (bottom) of the young-disk model, as measured 

using the Balmer lines. (b) The same as (a), but this time using the near-infrared Calcium triplet to determine the 

stellar kinematics. The young, fast rotating disk is clearly visible using the Balmer lines, whereas the old, nonrotating 

spheroid component is most apparent using the Calcium II Triplet. 

Figures 3-7 takes this example one stage further, showing simulated MUSE observations of 

the young disk embedded in the old spheroid, simulated for a single 1 hour exposure of a 

typical elliptical galaxy at a distance of 20 Mpc. Figure 3-7 (a) shows the mean rotation 

velocity and velocity dispersion of this two-component galaxy as observed using the spectral 

region containing the Balmer lines (3650-4950 Å). Here the rotation of the young disk is 

clearly visible, and the dispersion is low, as expected for a cold disk component. Figure 3-7 

(b) shows the rotation velocity and dispersion for the same MUSE exposure, but measured 

using the near-infrared Calcium II triplet. At this wavelength, the spectrum has a much higher 

contribution from the old, non-rotating spheroid, and the measured rotation is clearly reduced; 

likewise, the dispersion is correspondingly higher.



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This simple example shows how the extensive wavelength coverage of MUSE can be used to 

investigate one of the key questions in galaxy evolution: how the stellar populations are 

related to the dynamics of a galaxy? From the data delivered by a single MUSE exposure, it 

will be possible to model in detail the dynamical and chemical composition of galaxies 

simultaneously, combining dynamical modelling techniques (such as orbit-superposition or n- 

body) with modern stellar libraries and population models. Thus will give tight constraints on 

population mixtures and kinematic components, linking a galaxy's morphology, dynamics and 

star-formation history directly, in objects spanning the full range of galaxy environment. 

References 

Bacon, R. et al. 2001, MNRAS, 326, 23 

Baugh, C.M., Cole, S., & Frenk, C.S. 1996, MNRAS, 283, 1361 

Bruzual, G. & Charlot, S. 2003, MNRAS, 344, 1000 

Cappellari, M. & Emsellem, E. 2004, PASP, in press 

Cappellari, M. & Copin, Y. 2003, MNRAS, 342, 345 

Goto, T., Yamauchi, C., Fujita, Y., Okamura, S., Sekiguchi, M., Smail, I., Bernardi, M., & 

Gomez, P.L. 2003, MNRAS, 346, 601 

Kuntschner, H., Smith, R.J., Colless, M., Davies, R.L., Kaldare, R., & Vazdekis, A. 2002, 

MNRAS, 337, 172 

Mehlert, D., Thomas, D., Saglia, R.P., Bender, R., & Wegner, G. 2003, AAp, 407, 423 

Trager, S.C., Faber, S.M., Worthey, G., & González, J.J. 2000, AJ, 119, 1645 

Worthey, G. 1994, ApJS, 95, 107 

de Zeeuw, P.T. et al. 2002, MNRAS, 329, 513



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3.4. Interacting galaxies 

The study of galaxies in interaction 

has progressed dramatically in the 

last decades, showing evidence for 

a number of important processes 

occurring during these violent 

encounters: gas fuelling from 

kiloparsec to parsec scales, 

triggering of density waves such as 

large scale spirals and bars, 

starbursts. Tidal forces are efficient 

drivers of violent evolution and can 

easily produce bridges and tails, 

sometimes ejecting a large quantity 

of gaseous and stellar material in 

the intergalactic medium. Both the 

large-scale structure and the central 

Figure 3-8: WFPC2 image of the Antennae galaxy 

regions of on-going nearby galactic 

interactions are important to examine in detail as they can provide clues on the extent and 

distribution of e.g., the dark matter haloes (Bournaud, Duc, Masset 2003 and references 

therein), the stellar formation processes in extreme environments (e.g. super stellar clusters, 

SSCs; see e.g., Hunter et al. 2000), and more importantly on the building of galaxies in our 

hierarchical universe. Interacting systems are also the benchmark for our understanding of 

their higher redshift representatives. 

On-going mergers, like the Antennae galaxies, do fit in this context, and clearly, long-slit 

spectroscopy can only provide a biased and limited snapshot of these systems. Only 2D 

spectroscopy can reveal the full view of the rapidly evolving merger and e.g. constrain the 

interplay between the complex dynamics and the on-going star formation, thus combining 

detailed spectra of the newly born/forming clusters with those of the ionized gas medium. In 

this context, MUSE can bring unprecedented information on a number of exciting issues: 

• probing the structure, content and dynamics of the tidal tails and bridges in interacting 

systems. This could include the so-called Tidal Dwarfs galaxies (TDGs) which are 

observed at the tip of tidal tails, tens of kpc away from the centre of the merging 

system. The stellar surface brightness of these structures is low, from 21 to 25 

mag.arcsec -2 in the I-band, although reachable with long MUSE exposures. The most 

interesting targets for MUSE are however the ionized gas structures, associated with 

the corresponding tidal features. Recently Ryan-Weber et al. (2003a, 2003b) 

confirmed the presence of compact HII regions in the outskirts of galaxies, first 

detected via an imaging survey (SINGG, Meyer et al. 2003; see also Sakai et al. 2002; 

Gerhard et al. 2002). The luminosities of these regions is around a few 10-16 to 10-15 

erg.s -1 .cm -2 , corresponding to a star formation rate of only a few 10-3 Msun/yr. They 

are barely detected in the continuum with the SINGG R images having a detection



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limit around 10-18 erg.s -1 cm -2 A -1 . These star forming regions may reveal the tip of the 

iceberg of a large reservoir of gas, as they are often observed to be linked to HI tidal 

features. Such structures are expected to have been more common in the past, and to 

have participated in the enrichment of the intergalactic medium. MUSE could be used 

as a true spectroscopic explorer in this context, although it is worth noting that these 

sources will be at the limit of what MUSE can target. 

• Understanding the formation of super star clusters in tidal tails: SSCs are prevalent in 

a number of optical tidal tails and for some reason completely absent in others. The 

spectroscopic mapping of these clusters in the environment in which they form will 

tell us whether the formation of SSCs is linked to the overall mass distribution and 

dynamics in the outskirts of the colliding galaxy pairs, or if it is related to local 

physical criteria. 

• Probing the central regions of merging systems: stellar populations, HII regions, super 

stellar clusters (HR and LR). Although a systematic study of such systems along the 

Toomre sequence is hampered by e.g., their intrinsic diversity (Laine et al. 2003), 

MUSE exposures will allow obtaining exquisite details on the physical conditions 

within their central kilo-parsecs. 

References 

Bournaud, Duc & Masset 2003, A&A, 411, L469 

Gerhard et al. 2002, ApJ, 580, L121 

Hunter et al. 2000, AJ 120, 2383 

Laine et al. 2003, AJ, 126, 2717 

Meyer et al. 2003, MNRAS in press 

Ryan-Weber et al., 2003a, astro-ph/0311465 

Ryan-Weber et al., 2003b, astro-ph/031067 

Sakai et al. 2002, ApJ, 578, 842 

3.5. Star formation in nearby galaxies 

By regulating the stellar, gaseous, chemical, dust and radiant and mechanical energy content 

of galaxies, star formation is a driving force behind their evolution. Yet a fundamental theory 

of star formation within galaxies is still missing, which is indeed one of the major obstacles to 

building a coherent theory of galaxy formation. Amongst the fundamental unknowns are the 

star formation history, efficiency, duration and duty cycle of starbursts of different intensities, 

the importance and the dynamical drivers of self-triggering and propagation for the spatial 

and temporal evolution of the starburst, the impact of starbursts on the host galaxy's stellar 

and interstellar medium structure, the feedback onto the evolution of the starburst itself, 

whether there are multiple modes of star formation, i.e., in compact dense cluster and in a 

diffuse field star formation mode, and clearly the dependence of these unknowns on the local 

and global galactic properties, and on the large-scale properties of the environment. In nearby 

galaxies, the WF pixel size of MUSE roughly corresponds to the sizes of young stellar 

clusters in starburst (Meurer et al. 1995) and normal (Carollo et al. 1998) galaxies, and is 

much smaller than the typical diameters of giant HII regions, which are up to about 300 pc 

(Oey et al 2003).

Understanding the physical properties of the 

gas reservoir from which the stars are formed 

is key to understanding the physics of 

formation of stars. Ionization and shock fronts 

through the interstellar medium may cause the 

star formation to propagate spatially (e.g., 

Puxel et al. 1997), on timescales which are 

likely to depend on both the local physical 

conditions as well as the global properties of 

the host galaxies. Extinction-corrected line 

emission fluxes of hydrogen and metal 

forbidden lines are needed to construct 

diagnostic diagrams such as the 

log([OIII]/Hβ) line ratio against the 

log([NII]/Hα), log([SII]/Hα), or log([OI]/Hα) 

ratios; these diagnostics distinguish photofrom 

shock-ionized gas. The presence of 

non—photoionized gas has been detected in 

long-slit spectroscopic and kinematic studies 

(Martin 1998). However, because of the 

limited area coverage of long-slit 

spectroscopy, this cannot quantify the 

prominence and extent of the nonphotoionized 

gas within the starbursts. This 

limitation is overcome in detailed HST studies 

that combine metal- and hydrogen-line high 



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Figure 3-9: Example of nuclear star forming ring. 

Shown is a HST multi-color image of the center of 

NGC 4314 (credit: Benedict et al., and NASA). 

Visible are dust lanes, a smaller bar of stars, dust 

and gas embedded in the stellar ring, and an extra 

pair of spiral arms full of young stars. HR-MUSE 

will allow exploring issues such as the connection 

between dynamics and star formation properties of 

such rings. 

spatial resolution data to identify and quantify non-photoionized gas. However, with the 

HST, these studies can only be performed with extremely time-costly narrow-band imaging. 

As a result, only four starburst galaxies within 5 Mpc have been to date investigated with 

HST (Calzetti et al. 2003). In these four starbursts, the fraction of non-photoionized gas 

appears to represent at most a 20% of the integrated emission line spectrum. The HST data 

suggest however that the galaxy environment plays a crucial role in driving the detailed 

structure of the interstellar medium. 

By combining large field of view, high spatial and spectral resolution, and broad spectral 

coverage, MUSE-WF will be ideal for measuring fundamental diagnostics of the emitting gas 

which are key to understanding the regulating mechanisms for the production and propagation 

of star formation at all scales - from the kpc scales of superbubbles and outflows and 

superwinds, down to the ~10pc scales which probe the interfaces between the actual sites of 

the star formation and the ionization and shock fronts. The few galaxies studied with HST 

have typical Hβ, [OIII], Hα and [SII] 1σ detection limits over an area comparable to the WF 

MUSE spaxel of about 10 -17 erg s -1 cm -2 . In a 4h integration with the WF of MUSE, 

5σ spectra are obtained down to fluxes in the range 1.3 to 5 and 0.3 to 1 10 -18 erg s -1 cm -2 for 

the red and blue lines, assuming a point source or a diffuse emission distribution, respectively. 

MUSE will be therefore generally able to extend such studies to significantly fainter levels of 

emission. Such WF MUSE observations will allow to establish whether and how the fraction 

of non-photoionized gas depends on galaxy properties by allowing surveying large samples of



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nearby star forming galaxies spanning across the entire parameters space of, e.g., 

morphological types, metallicities, star formation rates and dynamical properties. 

Furthermore, even a small fraction of non-photoionized gas is key for tracing the location and 

morphology of possible large-scale shock structures. Cavities, shells, filaments, concentrated 

emission indicate whether and where large amounts of mechanical energy are being deposited 

in the interstellar medium, and thus where and how larger-scale phenomena such as 

superwinds are likely to originate. MUSE will allow the investigation of the location, 

geometry and intensity of such shock structures as a function of local and global –including 

dynamical- galaxy properties. 

MUSE will also allow studying the star cluster population resulting from and cohabiting with 

the reservoir of star forming gas. These young star clusters have been revealed in a variety of 

star forming environments which include cooling-flow galaxies, interacting/merging galaxies 

(see section 3.4), amorphous peculiar galaxies, and, quite interestingly, ~100pc-scale nuclear 

rings embedded in the cores of otherwise normal disk galaxies (e.g., Maoz et al. 2001; see 

Figure 3-9). Only with HST imaging it has been possible to resolve the young star clusters in 

these rings and study their stellar content. Even for nearby galaxies, these clusters are barely 

resolved by HST. The ages of the clusters are typically one to a few hundred Myr, their 

magnitudes are in the range –10 > M V > -15 and their V-I AB colors are typically between -1 

and 2.5. Possibly fed by large-scale dynamical instabilities, such star forming rings are 

thought to play a major role in the secular dynamical evolution of disk galaxies and in the 

formation of pseudo-bulges by concentrating stellar mass in the nuclear regions (e.g., 

Kormendy & Kennicutt 2004). The HR channel of MUSE will allow the simultaneous 

investigation of the reservoir of star forming gas, the stellar content of the young stellar 

clusters, and their dynamics. A 4h integration with a modest (3x3) binning of the MUSE-HR 

will allow to obtain 5σ spectra down to approximately V~19.2 mag/arcsec 2 , allowing to probe 

down to the typical cluster population. 

The MUSE studies at optical wavelengths of the on-going and recent star formation will be 

complemented by ALMA studies of molecular gas at similar spatial resolution, and by highresolution 

ALMA studies of the continuum emission arising from highly obscured regions of 

star formation. The information provided by MUSE will substantially contribute to building a 

physical basis, in terms of internal structure, energetics and evolution, for constraining future 

simulations -and thus the currently ill-known theory- of star formation, and for interpreting 

the observations of star forming galaxies at higher redshifts. 

References 

Calzetti, D., et al., 2003, (astro-ph/0312385) 

Carollo, C.M., et al., 1998, AJ, 116, 68 

Kormendy, J., Kennicutt, R., 2004, ARAA, in press 

Martin, C.L. 1998, ApJ, 506, 222 

Maoz, D., et al., 2001, AJ, 121, 3048 

Meurer, G.R., et al., 1995, AJ, 110, 2665 

Oey, M.S., et al., AJ, in press (astroph/0307230) 

Puxley, P.J., Doyon, R., & Ward, M.J. 1997, ApJ, 476, 120



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4. Stars and resolved stellar populations 


MUSE will contribute to the understanding of a number of areas which are the subject of 

much current research through the observations of marginally resolved stellar groups and 

aggregates, and we expect that a large fraction of the stellar community will benefit from this 

instrument. We can broadly divide the nearby science cases according to four main groups: 

• Young stellar objects, Star-ambient interaction 

• Massive spectroscopy (complementarity with GAIA) 

• Extragalactic stellar astrophysics 

• Extended emission-line ISM/local IGM studies 

It is important to stress that MUSE will provide a unique opportunity to pursue extragalactic 

stellar astrophysics in galaxies up to several Mpc distant, pioneering a research field and a 

technique which will be extended even further with the advent of Extremely Large 

Telescopes, such as OWL. MUSE is the research tool of choice to study dense stellar systems: 

star-forming regions, star clusters, the Galactic bulge, the Magellanic Clouds, the inner disk. 

It is also ideal for study of hot ISM/star formation interactions in nearby external galaxies. 

4.2. Early stages of stellar evolution 

In the early stage of their evolution, stars produce powerful jets and winds. Bipolar atomic 

jets are at the same time the most impressive and the most enigmatic phenomenon associated 

with the birth of stars. Their structure is highly complex and spans a wide range of scales, as 

illustrated in the Figure 4-1, where a bar denotes 1000~AU, or 2 arcseconds at the 500 pc 

distance of Orion. It can be seen that the jet: 

• is launched and collimated within the innermost parts (< 20 AU) of the circumstellar 

disk around the young T Tauri star (cf. HH 30 - top left panel; Burrows et al. 1996) 

• develops chains of knots with typical spacing of a few 100 AU and apparent opening 

angle of a few degrees (cf. HH34 – top right; Ray et al. 1996) 

• undergoes wiggles and large-scale interactions with the interstellar medium or with 

previous ejecta through radiative working surfaces (akin to hot spots in extragalactic 

jets) known as Herbig-Haro (HH) objects, on a typical scale of 20,000~AU i.e. 40 

arcsec at a distance of 500 pc (cf. HH47 - bottom panel; Heathcote et al. 1996). 

None of these three phenomena (jet launching, knot formation, wiggles and large working 

surfaces) is fully understood at present. Yet, they raise fundamental questions: 

• Are stellar jets ejected from the star, its magnetosphere, or the inner disk surface 

• Could they be the « missing agent » responsible for solving the so-called angular 

momentum and magnetic flux problems of star formation? 

• Do they affect significantly the structure of circumstellar disks, and should they be 

taken into account in updated theories of exoplanet formation and migration?



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• Does the jet launching process evolve with protostellar stage, from the embedded 

collapse phase to the optically revealed T Tauri phase? 

• What is the origin of jet knots and wiggles? Intrinsic variability/precession at the 

source or MHD current/kink instabilities in plasma jets? 

• Do observed properties agree with theoretical shock models? 

• What is the large-scale cumulative impact of stellar jets on the ISM in terms of 

turbulence, induced compression, and grain destruction? 

Figure 4-1 : HST optical images of jets from young stars. The bar corresponds to 1000 AU, or 2 arcseconds 

at the distance of Orion (500 pc). Top left: Jets are launched perpendicular to the accretion disk and 

collimated within 20 AU of the central T Tauri star. Top right: Chains of knots with typical spacing of a few 

100 AU (0.5'') appear along the jet beam. Bottom: Non-axisymmetric wiggles, filamentary sideways shocks, 

and large radiative working surfaces (Herbig-Haro objects) develop on scales of 1000 to 20,000 AU (2''- 

40''). [S II] is coded in red and Hα in green 

MUSE will provide a powerful new tool for the study of large-scale stellar jets: Its wide field 

of view can encompass in a single exposure a typical jet and bowshock system such as HH 47 

(50 arcseconds across), or the bright inner jet beam of HH 34 (30 arcseconds). Furthermore, 

MUSE's optical range is optimal for studying atomic stellar jets, which are characterized by a 

strong optical emission line spectrum including Hβ, [O III]5007, [N I]5198,5201, [O I]6300, 

[N II]6584, Hα, [S II]6716,6731, [CaII]7307, and various [Fe II] lines. Its broad spectral 

coverage over 0.465 to 0.93µm will allow simultaneous recording of all lines at each position, 

providing extremely accurate line ratios for physical diagnostics. For example, the [O



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III]/Hβ ratio is a crucial indicator of fast shocks with speed above 100 km/s, while [S II] 

6716/6731, [NII]/[O I], and [SII]/[O I] provide direct estimates of, respectively, the jet 

electronic density, ionization fraction and temperature, with much less dependence on the 

heating process than ratios involving Hα (e.g. Bacciotti & Eisloeffel 1999). At the same time, 

MUSE 's spectral resolution (about 100 km/s) will allow to map the 2D kinematics over the 

whole extent of these jets, of typical speeds of 200-500 km/s, to within 10 km/s (line centroid 

precision). Finally, MUSE's WFM angular resolution of 0.3–0.4'' will enable to separate 

individual emission knots, wiggles, and sideways shocks in the jet beam (cf. Fig. 4-1), which 

are otherwise blended in seeing-limited optical studies. 

This combination of broad line coverage, kinematics, and high angular resolution over a wide 

field of view will represent an outstanding gain in quality and information content over jet 

studies with other optical instruments: Wide-field spectro-imaging of HH flows with a Fabry- 

Perot (see e.g. Morse et al. 1994) is typically limited to 3 lines only (usually [S II] 6716,6731 

and Hα) due to the time overhead for stepping through each line profile, which severely limits 

the physical diagnostics and the shock modelling. Furthermore, the line ratios may be affected 

by variations in PSF, sky transmission, and instrumental drifts during the scan. Narrow-band 

imaging, e.g. with HST, is contaminated by imperfect removal of stellar light and nebular 

emission, and does not allow to resolve the [S II] 6716,6731 doublet nor to separate [N II] 

from Hα, precluding density and ionization diagnostics. It also does not give any radial 

velocity information. Finally, optical spectroimaging with long-slits (eg STIS) is limited to 

very narrow jet regions, and clearly cannot cover a large structure such as the HH 47 

bowshock, nor probe non-axisymmetric jet features. In addition, uneven slit-illumination 

effects introduce spurious gradients that depend on the line and need complex a posteriori 

corrections (Bacciotti et al. 2002). 

An original, fundamental contribution of MUSE will thus be to routinely provide the first 

complete, accurate set of optical line ratios and line centroids at each position of large-scale 

stellar jets, with a resolution of 0.4''. A 1800sec exposure will yield a S/N of 50 per 0.2'' pixel 

on an Hα line of surface brightness 10 -15 erg.s -1 .cm -2 .arcsec -2 , i.e. 10 times weaker than the 3 

bright large-scale jets that can be currently studied at high resolution (HH 34, HH 47, HH 

111; cf. Fig. 4-1). This unique capability will open a new dimension in the analysis and 

modelling of stellar jets, two domains where ESO research is at world-class level. 

Major breakthroughs will result on a number of pressing questions, developed in the 

following paragraphs. 

4.2.1. The magnetic field strength and shock conditions in the jet 

An important outcome of MUSE will be to constrain the shock speed, preshock density, and 

magnetic field strength as a function of position and velocity in large-scale jets, from 

comparison of resolved optical line ratios with grids of atomic shock models (see e.g. 

Hartigan, Morse, Raymond 1994). A similar method was used to estimate the magnetic field 

in the ambient gas, from seeing-limited studies of large jet bowshocks (e.g. Morse et al. 

1992). MUSE will yield the magnetic field strength in the jet, a crucial constraint for MHD 

ejection models. Its combined high angular resolution and spectroscopic resolution will be 

essential to avoid blending of individual jet knots, which would otherwise bias the line ratios.



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An illustration of this point can be found in Lavalley-Fouquet et al. (2000): While integrated 

line ratios at the base of the DG Tau jet could not be modelled with a single shock (Hartigan 

et al. 1995), spatially and velocity-resolved lines ratios at 0.4'' resolution obtained with the 

OASIS spectro-imager are well fitted with internal shocks of speed of 30-80 km/s and a 

transverse magnetic field < 1000 µG. 

4.2.2. Jet total density and the ratio of ejected to accreted mass 

While estimates of jet mass-loss rate from integrated line fluxes appear uncertain by 1-2 

orders of magnitude, accurate values may be derived from local estimates of the velocity and 

of the total density, derived from optical line ratio diagnostics of the electronic density and 

ionization fraction (e.g. Cabrit 2002). With the sensitivity of MUSE, this approach can be 

applied to many more jets than the favorite 3 targets of current detailed studies. MUSE will 

thus provide for the first time accurate mass-loss rates as a function of position and velocity in 

a representative sample of jets, allowing a major improvement in the determination of the 

ejection to accretion ratio - another crucial parameter for theoretical ejection models -. 

4.2.3. The origin of jet knots and non-axisymmetric wiggling 

structures 

MUSE will provide for the first time the 2D velocity field in large-scale jets at a resolution of 

0.3–0.4'', and even the full 3D-field when combined with proper motions from multi-epoch 

observations. These observations will probably represent the strongest tests ever for the two 

competing models of knot formation, namely: propagation instabilities (K-H, current-driven, 

kink modes...) versus source variability (precession, velocity variability, orbital motions), 

through comparison with the 2D and 3D hydrodynamical and MHD jet simulations conducted 

by european teams, and laboratory laser beam experiments. This modelling will also constrain 

the MHD cross-section of the jet, or the timescale and amplitude of intrinsic jet variability, 

providing indirect clues to the ejection process. 

4.2.4. Low-velocity halo and relation to molecular jets 

Both the HH 47 jet (Hartigan et al. 1993) and the small-scale jet from DG Tau (Lavalley et al. 

1997; Bacciotti et al. 2000) possess a lower velocity halo surrounding the fast, bright optical 

jet beam. It is yet unclear whether this halo traces a slow wind ejected from several AUs in 

the disk, or a shocked cocoon created by interaction of the jet with the ambient medium. 

MUSE's high sensitivity will allow to trace this slow component further away from the jet 

axis, estimate its momentum flux, and investigate its relationship to molecular counterparts 

studied in H2 lines in the near-IR (e.g. with SINFONI) and in CO lines with ALMA. Such 

studies will be crucial to understand the formation of molecular flow cavities, which appear to 

require a wider wind component around the collimated optical jet, and to constrain the 

outermost disk radius affected by the ejection process.



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4.2.5. The physics of jet working surfaces and interstellar shocks 

2D maps of the kinematics and line ratios in jet working surfaces at 1.3'' resolution, obtained 

with F-P imagers, reveal good agreement with theoretical expectations for a bowshock and jet 

Mach disk, and constrain the ambient velocity and magnetic field ahead of the bowshock, as 

well as the jet/ambient density ratio (Morse et al. 1992; 1993; 1994). However, these studies 

meet several limitations; the H α line flux used to derive preshock density depends on the illknown 

pre-ionization and reddening; the cooling regions are spatially resolved (0.5'' to 1''), 

introducing a shift between Hα and [SII] emission that complicates interpretation of the line 

ratio; only the brightest 3 jets in the sky can be studied in a reasonable time. MUSE 

observations will revolutionize this domain by providing a wealth of reddening-independent 

line ratios for shock diagnostics, an angular resolution higher by a factor 3, and access to a 

more representative sample of jets. Expected outcomes include: detailed tests of interstellar 

shock models by comparison with observed cooling distances and line offsets, accurate 

estimates of the jet mass flux at the Mach disk and of the momentum transferred to the 

ambient cloud, evaluation of elemental depletion (using e.g. [Fe II]/[S II] ratios) as signatures 

of grain destruction in shock waves, implications on molecule reformation in the compressed 

post-shock gas... 

4.2.6. Jets in pre-planetary nebulae 

Optical jets associated with shocks and HH objects have been recently observed in young 

bipolar planetary nebulae (e.g. by HST) suggesting that collimated mass-loss occurs in dying 

stars as well. The unique capabilities of MUSE are also perfectly suited to probe this 

enigmatic mass-loss process at the other extreme of stellar life, which is also far from 

understood. 

4.2.7. High-resolution studies of the jet base 

The high-resolution mode of MUSE is perfectly suited to complement studies of the 

innermost regions of jets with ALMA and SINFONI. In the mm range, ALMA will trace the 

cool (< 500 K) molecular flow at very high spectral resolution (0.1 km/s). In the near-IR, 

SINFONI will trace warmer molecular gas (2000 K) in H2 as well as hot atomic jets in [Fe II] 

and He I, with 75 km/s resolution. MUSE will trace the same atomic component at similar 

spectral resolution, but over a much larger field of view (7.5'' instead of 0.8'' for SINFONI in 

its 25mas mode). This unique feature will allow e.g. to follow the appearance and spacing of 

individual knots as they propagate along the jet, and to investigate the origin of enigmatic 

broad wind bubbles, such as that seen in XZ Tau (4'' in size; Krist et al. 1999). In a 3600sec 

exposure, MUSE will reach a S/N of 30 for an Hα brightness of 1.6 10 -14 erg.s -1 .cm - 

2 .arcsecond -2 , typical of the XZ Tau bubble and of T Tauri jets at 0.5'' from the star. A S/N of 

7 will be achieved for 10 -15 erg.s -1 .cm -2 .arcsecond -2 , allowing to probe more distant regions, or 

fainter lines. Another complementary feature of MUSE is its wide spectral band, including 

powerful density, temperature and ionization diagnostics. The innermost (< 0.5'') regions of T 

Tauri jets are bright enough in the optical (up to 10 -13 erg.s -1 .cm -2 .arcsecond -2 in [O I]6300) to 

be detected by MUSE in a variety of line diagnostics. Serendipitous line detections may also 

show up in this wide band, which has never been completely explored in stellar jets.



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4.3. Massive spectroscopy of stellar fields: our Galaxy and 

the Magellanic Clouds 

A continuing challenge for observational astrophysics is the detailed study and understanding 

of the star formation and the chemical history of the Galaxy and of its nearby companions, 

such as the Magellanic Clouds. There is the opportunity for a substantial European 

astronomical community leadership in this crucial science, by combining appropriate 

instrumentation at ESO with the forthcoming ESA GAIA mission (see, e.g., Perryman et al. 

2001 for a more comprehensive presentation of the GAIA science cases). GAIA will provide 

high spatial resolution (0.2arcsec) and precision astrometric data for the whole sky to V=20. 

By complementing this information appropriately, we will be well placed to make 

quantitative advances in addressing basic questions: How did our galaxy and its satellites 

form? How have they evolved? What is the stellar population history of the Galactic Bulge? 

The answer to these basic questions requires an enormous observational effort, but can be 

done in detail at low redshift, to calibrate and complement the direct studies at higher redshift. 

While massive photometry surveys (MACHO, EROS, 2MASS, DENIS, VST, VISTA) have 

paved the road towards a better census, most of the fundamental kinematics and spectroscopic 

information is so far missing. Without this, our knowledge will necessarily remain limited, 

with no adequate inclusion of chemical abundances, useful ages, or the critical kinematics, 

mapping both the gravitational potential and the orderliness (or otherwise) of accretion and 

evolution: these limitations can be seen for example, from the restrictive analysis possible of 

even the massive photometry data sets in the LMC/SMC (Zaritsky et al.1999). 

4.3.1. The astrophysics of crowded regions and GAIA 

complementarity 

The GAIA mission will ultimately measure parallaxes and proper motions for a billion stars 

up to V~20 with unprecedented accuracy, and will obtain radial velocities for relatively 

isolated bright (V



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will allow spectroscopy of the X-ray candidates, to confirm their nature, and to measure and 

determine their orbital parameters. Similarly, spectroscopy could reveal the nature of the 

many blue stragglers and blue objects revealed in the HST images of the core of 47 Tuc 

(about 100 objects found by Ferraro et al. 2001) , and alleviate the current bias to extremely 

hot objects: it is reasonable to assume the existence of strange cool objects too. It may even be 

possible to understand the anomalous evolution of globular clusters recently discovered 

(Mackey and Gilmore 2003), where it seems core evolution proceeds in the opposite sense to 

that expected from dynamical predictions with no allowance for physical interactions. 

The key experiment here is dynamical mapping of the inner regions of Globular Clusters 

across the full age range uniquely available in the Magellanic Clouds. Such data would 

determine kinematic distribution functions, binarity, mass length scales, the incidence of 

extreme objects, the age-dependance of core mass-transfer hard binaries, and so on, providing 

a unique view of the dynamical evolution of dense systems. 

Figure 4-2: The inner Galaxy extinction map, derived from DENIS survey data (Schultheis etal 2000). The many 

areas of low extinction, on a scale of the MUSE fov, are apparent, illustrating that quantitative dynamical and 

stellar population studies of the inner Galactic bulge and Old Disk, are feasible using optical spectroscopy. 

Essentially nothing is understood of the evolution of the dense inner Galactic Bulge, clearly a 

site of continuing massive star formation, where sufficiently many optical windows are 

known to allow short-wavelength studies (see Launhardt, Zylka and Mezger 2002; and Yusef- 

Zadeh, Melia and Wardle (2000) for recent overviews of the inner bulge astrophysical zoo). 

In all these cases, it is the combination of field of view – ideally matched to the physical 

scales of relevance – and full 2-D sampling, allowing deconvolution of disparate sources, 

which makes the science viable. This same science of course can be extended, at decreasing 

physical spatial resolution, to other nearby galaxies and their nuclei. 

Many other fields exist for which MUSE will allow detailed astrophysical analyses, provided 

that massive spectroscopic studies are available. Fields in the Magellanic Clouds and in the 

Galactic bulge are obvious candidates, because these aggregates are close enough to allow 

the sampling of a good fraction of the Colour Magnitude diagram and at the same time they 

represent unique stellar systems.



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4.3.2. Surveying the Large 

Magellanic Cloud 

As a close companion to our Galaxy, the 

Large Magellanic Cloud has been under 

extensive scrutiny with studies focusing on a 

broad range of scientific issues, including e.g., 

star formation regions, micro-lensing, HI 

structure, calibration of the distance scale... 

Considering the simultaneous spectral and 

spatial coverage, MUSE could significantly 

contribute to our understanding of the stellar 

populations and dynamics of this galaxy. 

Apart from a better handle on the stellar 

populations and the corresponding star 

formation history of the LMC itself 

(influenced by past encounters with our 

Galaxy?), MUSE data on the LMC could 

provide a unique handle on its intrinsic 

structure. The LMC is indeed a complex 

object, with e.g., an offset bar lying near, but 

not at, its centre (see van der Marel et al., 

2002; and references therein) and the lack of 

kinematic data is only emphasizing it more. 

Only about 1000 carbon stars so far have 

their radial velocities measured to yield 

some constraints on the line-of-sight 

kinematics of the LMC (see e.g. Alves & 

Nelson, 2000). A more detailed knowledge 

of the LMC stellar population, internal 

kinematics and morphology will have 

important consequences on scenarios for 

galaxy formation, the formation of our own 

Galaxy halo, results from micro-lensing, etc. 

The full spectroscopic mapping of even just 

the central bar about 3x0.5 degrees) would 

ask for a prohibitive amount of telescope 

time. However, a ‘sparser’ approach in 

terms of spatial locations of the fields could 

uniquely probe the LMC structure, with only 

a reasonable scientific loss due to the noncontiguity 

of the fields. According to the 

luminosity function derived by Smecker- 

Hane et al. (2002), we expect a density per 

MUSE field (1 arcmin 2 ) and per magnitude 

bin of roughly a few stars at V=19 and more 

Figure 4-4: Colour magnitude diagrams obtained 

with WFPC2 observations. A) Disk field about 2 

degrees from the center of the LMC bar and b) the 

bar field. Extracted from Smecker-Hane et al. 

2002. Panels c) and d) magnify the red clump 

region. 

Figure 4-3: Main sequence luminosity 

function of the LMC in the bar and disk 

fields observed by Smecker-Hane et al. 

(2002). Bin size are 0.05 mag. Model 

luminosity functions are also provided 

(constant star formation rate – solid lines, 

see Smecker-Hane et al. 2002 for details).



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than 100 stars at V=22 mag within the bar of the LMC (see also Elson et al. 1997). This 

obviously decreases rapidly outwards, with typical density 10 times smaller in the disk of the 

LMC. In order to observe tens of stars in a single MUSE exposure a few degrees from the 

centre of the LMC bar, we need to reach V=22.5 mag, which is feasible with short exposures 

of less than 600s in the WFM, at a spectral resolution of 500 (using the NoAO mode with 

‘’good’’ observable conditions, and spectral binning). Conversely, this will bring hundreds of 

stars per MUSE exposure in the central bar region, without the need to spectrally bin for the 

brightest ones. 

As shown in the colour-magnitude diagrams of e.g., Smecker-Hane et al. (2002), a limit of 

V=22.5 mag will allow to reach stars 1.5 magnitude below the oldest main-sequence turnoffs 

in the LMC. A knee is observed in the luminosity function of both the disk and the bar around 

V~22.2 mag. There are also a number of spikes in the bar luminosity function around V=21.5, 

20.6 and 19.7, indicating large temporal variations in the star formation rate, all easily 

reachable with short (few mn) MUSE exposures. A large observation campaign aimed at 

probing both the bar and disk stellar populations and kinematics (with different exposure 

times) would thus provide an unprecedented (and simultaneous) view at the star formation 

history and internal structure of the LMC. Note that the use of AO would certainly minimize 

the potential blending effect, and that the spectral information provided by MUSE will also 

allow a relatively easy decontamination from foreground Galactic sources. 

The main goal of such an ambitious project would be to 1) obtain spectral information of stars 

spanning a significant coverage (statistically speaking) of the HR diagram, significantly 

below the oldest main sequence turnoffs, and down to the observed ‘’knee’’ at V=22.2 mag 

hence constraining the entire star formation history of the LMC, 2) spatially cover both the 

disk and the bar of the LMC hence properly constraining the intrinsic structure of the galaxy 

and its link to its stellar population (e.g., the hypothesis that the bar formed 1 to 2 Gyr after 

the disk). This goal requires spectra for at least 50 000 stars, with the entire visible spectral 

Fig. 4-5: Combined FORS (Red) and HST(Blue) images of the young LMC Cluster NGC1850; emission 

line filaments (Hαl ) are clearly present; the small group of hot stars below the main cluster (NGC1850B) is 

younger, only a few Myrs old. Narrow band photometry has revealed a population of T-Tauri candidates, 

lying preferentially along the filaments (Romaniello et al. 2002). Only spectroscopy can probe their nature.



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domain at a resolution R > 800 (and up to 3000 for the brightest ones). Most of these stars 

will be fainter than V = 20 mag, hence this calls for a large telescope aperture. VIMOS, in its 

IFU mode, would not be efficient enough (field is too small for the 0.33 arcsec per fiber 

mode, at intermediate spectral resolution around R=700), would require long exposure time (> 

30 mn) to reach the desired magnitude limit (V=22.5), and would only cover part of the 

visible spectral domain. The MOS mode of VIMOS is also excluded because of its low 

overall efficiency with the need of pre-imaging, its limited spectral coverage and its inability 

to cope with dense fields. 

Using the constraints mentioned above, we can make a first estimate of the amount of 

telescope time to be devoted to such a survey of the LMC stellar populations and structure: it 

would require about ¼ of a square degree (1000 individual MUSE fields, separated by about 

15 arcmin on average) with an average of about 8 mn exposure time, hence a total of about 

135 hours. 

In Figure 4-5 a composite FORS and HST image of the NGC1850 cluster in the LMC is 

shown (Romaniello et al. 2002). Clusters like these represent unique chances for studying 

star formation in action, as well as in its later `feedback’ phase reheating the ISM: critical 

information to improve galaxy formation recipes. Such objects are very rare, are large in area, 

are crowded, and are complex stellar and gaseous environments. While the main cluster is 

several tens of million years old, the blue aggregate just below (NGC1850B) probably 

represents a much younger star formation region. Was the star formation in the young cluster 

triggered by shocks from the older one? Does a population of lower mass stars (T-Tauri) exist 

and what is its IMF? Did the formation of young low mass stars preferentially take place 

along the filaments created by the SN shocks? To answer all these questions, 3D spectroscopy 

is necessary, coupled with high angular resolution; in cases like this 3D is much superior to 

single object spectroscopy because it will allow one to simultaneously map the emission gas, 

derive its dynamics and to disentangle the gas 

Hα emission from that of the T Tauri 

candidates: ie, to quantify `feedback’. 

Most of these sources are relatively bright, so 

that very long exposures are not required: 

rather, high S/N studies will be possible. The 

unique contribution of MUSE is in combining 

high image quality with areal coverage, 

essential for progress when the answer is not 

known in advance, since 

deconvolution/modelling of the actual 

luminosity distribution in the field under the 

seeing conditions of observation is critical for 

the science: ie, quantitative IFU imagingspectroscopy 

is critical. 

Figure 4-6: The current state of knowledge of corecollapse 

supernovae progenitors. Only three good 

identifications are known, two discovered in late 

2003. A systematic study of massive stellar 

populations with MUSE would quantify this figure, 

and quantify the origin of compact objects: neutron 

stars and black holes – in the Universe.



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4.3.3. Supernovae remnants 

The field around SN1987A is an excellent example: in this field HST photometry starts to 

unveil the nature (and reddening) of some 21000 stars down toV~24 (about 400 stars.arcmin -2 

at V=22, Panagia et al. 2001); needless to say that MUSE (+AO) spectroscopy could confirm 

the photometric results, but more importantly determine the age distribution of the stars in the 

SN1987a field. This, in turn, will permit one to determine if the SN progenitor was part of a 

12 Myr old loose cluster as suggested by the HST photometry. More generally, Smartt etal 

(2004) and Maund etal (2004) have proven a method to identify the precursors of TypeII 

supernovae, by (HST) imaging of nearby star-forming near face-on spiral galaxies. This 

technique has already led to the first planned discoveries of SN precursors: a MUSE 

spectroscopic survey to complement the available multi-colour imaging would revolutionise 

not only knowledge of the very late stages of stellar evolution, but extend studies of massive 

star formation into a systematic stage of quantitative exploration across the spiral sequence. 

Fig 4-7: M81, host to SN1993J, whose surviving binary companion star is shown. Studies such as these, 

which identify supernova precursors from archival imaging, could be made quantitative through a massive 

spectroscopic survey of nearby star-forming disks. [from Maund etal 2004] 

4.4. Massive spectroscopy of stellar fields: The Local group 

and beyond 

The study of resolved stellar populations in galaxies out to the distance of the Virgo cluster 

has become a major science case for the proposed new generation of OWL and other 

Extremely Large Telescopes (Hawarden et al. 2003, Najita & Strom 2002, Wyse et al. 

2000). MUSE will contribute dramatically to the study of galaxy origins and evolution by 

surveying large volumes of the distant, early Universe. In parallel, only the investigation of 

nearby galaxies through detailed analysis of their stellar populations, resolved into individual



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stars, can provide quantitative templates for the calibration of integrated light studies of 

higher redshift systems. 

Pioneering work (Fig. 4-8) with conventional MOS techniques has shown that these 

observations are extremely challenging due to source confusion in crowded stellar fields and 

severe contamination from gaseous emission of the ISM. Integral field spectroscopy has the 

unique potential to overcome these limitations, applying, in principle, the same methods 

which have been developed so successfully for crowded field CCD photometry (e.g. 

DAOPHOT). As an additional asset, IFU surveys in nearby galaxies will provide a wealth of 

serendipitous discoveries, in particular emission line stars, novae, planetary nebulae and H II 

regions, luminous Xray sources, etc. 

Fig. 4-8 Resolving galaxies into stars: one degree field of local group galaxy M33 with LBV 

candidate star B416 and surrounding nebula. Prototype 3D spectroscopy in the highlighted 

15”x16” field, conducted at the Selentchuk 6m telescope, has demonstrated the superiority of the 

method over conventional slit spectroscopy. 

The conventional analysis of stellar populations in external galaxies for star formation 

histories and chemical enrichment using the classical methods of resolved stellar CCD 

photometry on the one hand, and integrated-light broad band colors or absorption line indices 

on the other, suffers from well-known shortcomings such as the age-metallicity degeneracy, 

and uncertainties from the presence of dust and ionized gas in the interstellar medium of the 

galaxy under study (dust effecting the surface photometry through extinction, gaseous 

emission filling in the absorption line profiles of Hβ, Mg b , Mg 2 ). Other observational 

limitations for absorption line indices are related to systematic errors of long-slit spectroscopy 

(Mehlert et al. 2000).



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A more recent alternative approach is 

based on the spectroscopic analysis of 

individual resolved luminous stars in 

nearby galaxies, e.g. M31 (Smartt et al. 

2001, Venn et al. 2000), NGC6822 

(Venn et al. 2001), M33 (Monteverde et 

al. 1997), or NGC300 (Urbaneja et al. 

2003). Using VLT + FORS, the 

feasibility of high signal-to-noise stellar 

spectroscopy even beyond the local 

group was demonstrated by Bresolin et 

al. (2001), who measured 7 supergiants 

of spectral types B, A, and F with 

V≈20.5 in NGC3621 (d=6.7 Mpc). For a 

review on extragalactic stellar Fig. 4-9: Removing nebular contamination from 

stellar spectrum using cplucy 

spectroscopy, see Kudritzki 1998. 

Using its potential for crowded field spectroscopy, which is superior to any other conventional 

technique, MUSE will explore the emerging field of extragalactic stellar spectroscopy as an 

important step towards the optimal use of the combination of light-collecting power and 

angular resolution of these future telescopes, whose importance for applying the wellestablished 

methods of quantitative stellar spectroscopy to stars in galaxies outside of the 

Milky Way must be stressed as one of the major innovations in astrophysics of the next 

decades. 

The main argument is that the knowledge of the point-spread-function (PSF) of a stellar 

object can be used to apply PSF-fitting techniques, thus discriminating the source against the 

background ⎯ analogous to PSF-fitting CCD photometry, which has been so successful for 

the construction of globular clusters CMDs and the photometric study of resolved stellar 

populations in nearby galaxies (Mateo 1998). The novel technique has been pioneered with 

relatively small present-day IFUs and limited angular resolution (Roth et al. 2003, Becker et 

al. 2003). These studies have demonstrated the unique capabilities which can be expected 

from the 1 arcmin FOV of MUSE, sampled at 0.2” spatial resolution (or its equivalent in the 

Narrowfield Mode). 

Becker et al. (2003) have processed datacubes of the LBV candidate star B416 with its 

surrounding nebula in M33 using the cplucy two-channel deconvolution algorithm to separate 

the stellar spectrum from a spatially unresolved nebular component (Fig.4-9). This technique 

makes use of the spatial resolution of an HST image of the same field, providing for an 

accurate model of the heavily blended stellar field from the ground-based 3D observations. 

Fig. 2 shows how it was possible to accurately subtract from the stellar spectrum the 

contaminating [O III] λ4959, λ5007 emission lines, revealing He I λ5015 and a blend of Fe I 

lines. This result would have been impossible to obtain from conventional slit spectroscopy, 

demonstrating that the 3D method opens entirely new opportunities for crowded field 

spectroscopy.



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The correction for nebular contamination is a pressing issue for the quantitative spectroscopy 

of massive stars in nearby galaxies, limiting very severely our ability to provide the badly 

needed statistics to shed light on stellar evolution for masses > 10 M◉ , which is theoretically 

poorly understood. Likewise, abundance studies of extragalactic planetary nebulae, which 

provide important information about star formation histories and chemical enrichment, suffer 

dramatically from systematic errors due to background subtraction problems in high-surface 

brightness regions of local group galaxies and beyond. Large area, high spatial resolution 3D 

spectroscopy is the clue to solving these observational problems. The two examples are 

discussed in more detail in the sections below. 

A deep mosaic survey over 5x5 arcmin 2 with a total observing time of 100 hours per galaxy 

will result in an unprecedented inventory of O-B-A supergiants, rare 

LBV−WN/Ofpe−B[e]−WN−WC stars, planetary nebulae, and H II regions for any galaxy of 

the Fornax group, providing simultaneously : 

• complete spectroscopic samples for the quantitative study of these object classes 

• a unique database for the calibration of long-range integrated-light stellar population 

diagnostics, based on first principles (accurate abundances, ages, and kinematics from 

individual stars/nebulae) 

Note that the survey will be extremely efficient in that it replaces the conventional way of 

targeted observations for any single object class by a single campaign. For example, the 

Massive Stars and PN science cases as described below are covered by the same survey. In 

addition, data mining will provide spectra for other objects like SNR, novae, ultra-luminous 

X-ray sources, the diffuse ISM, etc., and has a highly interesting potential for serendipity 

discoveries. Complementary HST/ACS multicolour imaging for the obvious target, the 1000 

nearest star forming high-inclination galaxies, is being obtained as part of the Smartt-Gilmore 

Supernova Progenitor program, as is much direct VLT imaging. We will of course have VST 

and VISTA imaging available on the same time scales. 

Note also that qualitatively the survey will be superior to any other ground-based survey of 

nearby galaxies, since it provides the advantage of high spatial contrast source discrimination 

(“crowded field 3D spectroscopy”, see above), and an order of magnitude higher spectral 

contrast than typical narrow-band imaging surveys. 

The combination of large field-of-view with seeing-limited spatial sampling (WFM) makes 

MUSE an unrivalled tool for background-limited spectroscopy of resolved stellar populations 

in nearby galaxies: 260× more efficient than FLAMES, and 210× more efficient than the 

GMOS-IFU. 

4.4.1. Stellar evolution of the most massive stars 

How the most massive stars evolve from the main-sequence and produce populations of blue 

and red supergiants, luminous blue variables, Wolf-Rayet stars, and finally the core-collapse 

supernovae Types II, Ib and Ic is not well understood. The initial metallicity of the stars is a 

key ingredient, and will affect star formation, mass-loss rates, rotation and overall evolution. 

We need to go beyond the Magellanic Clouds (0.5Z 

and 0.2Z 

) to probe extreme systems. 

Massive star evolutionary phases last a very short time and one must expend a lot of telescope



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time to spectroscopically observe every luminous star in a star-forming region. Because these 

regions are generally crowded, this has not been done with conventional spectrographs in 

single or multi-slit mode. Integral field spectroscopy with AO correction is the ideal approach 

to ensure completeness. 

Extensive work is underway with the VLT on identified single stars, using FLAMES. Much 

HST imaging is underway. However, all this, while valuable, essentially assumes that 

interesting sources are already known: in fact, the bolometric correction for very hot sources 

is such they are not ab initio knowable: complete spectroscopic surveys of star forming 

regions are essential for reliable analyses. 

Fig. 4-10: Examples of southern nearby disk galaxies, suitable for a census of massive stars: NGC45, 

NGC55, NGC247, NGC253, NGC300, NGC7793 (left-right, top-bottom). The DSS frames subtend a FOV 

of 5x5arcmin 2 . 

Massive stars play a key role in the chemical enrichment of galaxies as well as in the 

dynamics of the interstellar medium by their large input of momentum and kinetic energy and 

their radiative luminosity. The understanding of the evolution of galaxies depends on our 

knowledge and understanding of the evolution of massive stars. Unfortunately, this evolution 

is not well known. We roughly understand the overall trends and sequences from evolutionary 

calculations (e.g. Maeder et al. 1991, Meynet& Maeder, 2000), but the observations show 

several classes of massive stars that do not properly fit into these evolutionary schemes. The 

goal is to unravel the evolution of massive stars by observing and studying large numbers of 

massive stars, and fitting them into evolutionary schemes by using new state-of-the-art 

evolutionary calculations, including rotation. 

Several classes of massive stars are known: 

• the luminous O and B stars



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• the Luminous Blue Variables (LBVs) with their large variability on many timescales 

• the B-type hypergiants which have spectra identical to those of LBVs, but do not 

show the large variability 

• the B[e]-supergiants with their outflowing disks 

• the WN/Ofpe stars with their strong emission lines 

• the Wolf Rayet stars with their N-rich (WN), C-rich (WC) or O-rich (WO) spectra. 

• there are peculiar massive binary objects with relativistic stars and accretion disks, e.g. 

SS433 and Cyg X-3. All these classes of stars show emission lines in their optical 

spectrum. 

It is generally accepted that the evolution starts with “normal” O and B stars in the main 

sequence phase and ends with the Wolf-Rayet (WR) phase, before the stars explode as 

supernovae (e.g. Maeder and Conti, 1994; Lamers et al. 1991). However, it is not clear how 

and where other observed classes of massive stars fit in the evolutionary scheme: H-rich WN 

stars, LBVs, hypergiants, B[e]-supergiants, Ofpe/WNL stars. Studies of massive stars in the 

Milky Way have not provided a conclusive picture for the problem since extinction in the 

galactic plane prevents systematic studies of sufficent numbers of stars of these different 

classes. For instance, only five confirmed LBVs have been found in our Galaxy (Humphreys 

and Davidson, 1994) and only two confirmed B[e]-stars (Lamers et al. 1998). In addition, the 

distances and hence the luminosities of the massive stars in our Galaxy are not always known 

with sufficient accuracy to compare different types of stars. The study of the LMC and SMC 

provides a view with little extinction of stars at the same distance. However, there the 

numbers of massive stars is small, except in the very young 30 Doradus region, which 

contains no LBVs and B[e]-stars yet. 

Objectives: 

The immediate goal is the discovery and systematic study of a large number of H α emitting 

stars with 3D spectroscopy, in typically 5x5 arcmin 2 fields of nearby southern disk galaxies, 

i.e. the Sculptor Group galaxies NGC55, NGC247, NGC300, etc. The final aim is to unravel 

the evolution of massive stars, by means of a careful study of the properties, interrelations and 

relative numbers of different classes of massive stars and comparing these with new stellar 

evolution calculations. The study combines the very powerful method of 3D spectroscopy 

with deep photometry and high resolution HST direct imaging. 

Feasibility: 

A table of signal-to-noise estimates for a range of typical massive stars at a distance of m-M = 

26.53 (Fornax group, Freedman et al. 2001) obtained with total exposure times of 4 hours and 

1 hour per field, respectively, is listed below. The corresponding absolute visual magnitudes 

for this distance are also listed in the last column.



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Tab. 4-1 S/N estimates at 5500 Å for different exposure times, R=2000 

M V 

V Exposure [sec] S/N 

d=2Mpc 

20.7 4x 3600 100 -5.83 

21.9 4x 3600 50 -4.63 

23.9 4x 3600 10 -2.63 

19.3 1x 3600 100 -7.23 

23.2 1x 3600 10 -3.33 

@ 

Tab. 4-2 shows for different masses of 20 … 120 M ◉ the evolution of spectral type and 

absolute visual magnitude MV with age (adopted from Massey 2003). At the distance of 

Fornax, the stars for which deep 4 hour exposures will yield high S/N (~100) are shown in 

orange, medium S/N (~50) in yellow, and low S/N (~10) in beige. 

120 M ◉ 

85 M ◉ 

60 M ◉ 

40 M ◉ 

25 M ◉ 

20 M ◉ 

Tab. 4-2 

0.0 

Myr 

-6.2 

O3 V 

-5.7 

O3 V 

-5.2 

O4 V 

0.0 

Myr 

-4.6 

O6 V 

-3.8 

O8 V 

-3.5 

O9.5 V 

0.5 

Myr 

Evolution of massive stars at galactic metallicity 

1.0 1.5 2.0 2.5 3.0 

Myr Myr Myr Myr Myr 

-6.9 

-6.6 

-7.0 -8.6 

O5.5 

O4 III O5 If WNL 

III 

-6.4 

O3 V 

-5.9 

O4 V 

-5.4 

O5 V 

1.0 

Myr 

-4.8 

O6.5 V 

-4.0 

O8 V 

-6.1 

O4 III 

-5.5 

O5 V 

2.0 

Myr 

-5.1 

O7 III 

-4.1 

O9 V 

-3.7 

O9.5 V 

-6.4 

O5.5 

III 

-5.7 

O5.5 

III 

3.0 

Myr 

-5.5 

O8 III 

-4.3 

O9 V 

-6.9 

O7 If 

-5.9 

O6.5 

III 

4.0 

Myr 

-6.6 

B0.5 I 

-4.6 

O9.5 

III 

-4.0 

B0 V 

-7.9 

B0 I 

-5.9 

O6.5 

III 

5.0 

Myr 

-4.9 

O9.5 

III 

-6.3 

O7.5 If 

6.0 

Myr 

-5.6 

B0.5 I 

-4.4 

BO III 

-7.2 

B0 I 

8.0 

Myr 

-5.3 

B1 I 

As can be seen in Tab.4-2, all of the rare high mass stars can be observed with very good S/N, 

allowing for quantitative spectroscopic analysis. In the mass range of 20-25 M◉, supergiants 

are observable with good to very good S/N. Wolf-Rayet stars typically have absolute visual 

magnitudes of about M V = -4.0, with WN stars covering a larger range of ~-2.5 … -7 (Massey 

2003). The detection limit for the purpose of classification is nevertheless fainter than this, 

since the essential criterion is the presence and strength of emission lines: He II 4686 (WN), 

and CIII 4650 (WC). Owing to the comparatively high spectral resolution, MUSE 

observations will have a clear advantage over conventional direct imaging searches,



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employing normally filter bandwidths of ~30 Å, which is an order of magnitude larger than 

the effective bandwidth of a monochromatic map from a MUSE datacube. 

Most WR stars outside the Galaxy have been found in the LMC (134), but only 8 in the SMC, 

and further 141 stars in M33, 48 in M31, 4 in NGC6822, 1 in IC1613, and 26 in IC10 (see 

review by Massey 2003 and references therein). Typical surface densities are ranging from 

~0.7 kpc -2 up to 10-40 kpc -2 , depending on star formation activity and metallicity. The 

proposed deep mosaic survey of the southern galaxies NGC45, NGC300, NGC7793, etc. will 

result in a number of massive star detections on the order of 1000 per galaxy, and for the first 

time provide a statistically significant inventory, which presently does not exist because of the 

small number of known objects. The existence of such a statistical meaningful database is a 

prerequisite before any break-through in the quantitative description of stellar evolution of 

massive stars can be expected ⎯ with important consequences for the input physics of 

models like Starburst99 for use in extragalactic astronomy (Leitherer et al. 1999, Smith et al. 

2002). 

It has recently been shown that the intrinsic luminosity of a massive blue supergiant star is 

closely correlated with its wind-momentum. This is termed the Wind Momentum – 

Luminosity Relation (WLR), and will potentially allow independent distance moduli to be 

obtained to an accuracy of ~10% to spiral galaxies within 10-15Mpc. (Kudritzki et al. 1999, 

Smartt et al., 2001, Bresolin et al. 2001). MUSE in its highest spectral resolution mode, 

together with AO correction will provide an unprecedented advantage over conventional 

spectrographs. 

There are a number of galaxies within the Local Group that have massive stellar populations 

at very low metallicities, providing close comparison to star formation in the early Universe. 

For example GR8, LeoA, SexA, have metallicities ~0.03Z 

and probing the massive star 

content would give better abundances ratios, complete IMF and star counts in evolutionary 

phases, and allow mass-loss to be determined at extremely low metallicities. IFU plus AO 

correction would be an ideal, and unique approach. 

The evolved descendents of massive O-type main-sequence stars (M 

≥ 20M 

) include the B, 

A and F-type supergiants which are the visually brightest, stable stars in the Universe. Unified 

model atmosphere theory allows abundances of C, N, O, Mg, Na, Si, S, Al, Ti, Fe, Cr, Sr, Zr. 

to be measured in their atmospheres. Crucially they probe the iron-peak elements, allowing 

determination of the α/Fe ratio, which is a key probe of galactic chemical evolution. 

Observations of these stars together with spectral synthesis will constrain galaxy evolution. 

4.4.2. Planetary Nebulae 

Extragalactic planetary nebulae (PNe) have been shown to possess unique potential for the 

study of the star formation history and chemical evolution of galaxies, based on the analysis 

of individual objects (Dopita 1997, Richer et al. 1999, Jacoby&Ciardullo 1999). They are 

particularly suitable to measure abundance gradients in elliptical galaxies where massive stars 

or H II regions cannot be used. Coupled with radial velocities which are easily measured from 

the bright [O III] 5007 line, PNe provide an ideal tool to investigate the merger history of 

NGC5128, where a total of 1140 PNe have been discovered to date. While conventional MOS



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instruments at 8m class telescopes are most efficiently used to measure the halo PNe, this 

technique fails completely in the high surface brightness regions near the nucleus (Walsh et 

al. 1999). Crowded Field 3D Spectroscopy is the only method to provide the required 

accuracy for background subtraction in this galaxy. The combination of high spatial 

resolution, 1’ FOV, large wavelength coverage, a suitable spectral resolution (R~1500) and 

high efficiency will make MUSE an unchallenged instrument for these observations. 

Fig. 4-11: Planetary Nebulae in NGC300, discovered with [O III] onband/offband imaging technique, 

using SUSI at the NTT. The frame in the color composite picture of the galaxy indicates the 2.2x2.2 arcmin 2 

FOV of the CCD camera. [O III] onband (top) and offband (bottom) frames are shown to the right. Several 

examples of XPN are indicated in the onband image (left to right): objects #27 (26.07), #23 (25.69), #2 

(23.00), #7 (23.25), #13 (24.38). From Soffner et al. 1996, m 5007 magnitudes in paranthesis. The total 

exposure time of the onband frame is 1800 sec 

XPN are ideal tracers of intermediate age and old extragalactic stellar populations, because 

their hot central stars are among the most luminous stars in the HRD, emitting their radiation 

predominantly in the UV. A substantial fraction (of order 10%) of the total luminosity is reemitted 

by the surrounding nebula in a prominent emission line spectrum, which gives enough 

contrast (for the bright lines) to detect the object as a point source against the bright 

background of unresolved stars of the parent galaxy. A practical application of this property 

has consisted in narrow-band imaging spectrophotometry, centered on the bright emission line 

of [O III] λ5007, and the construction of PN luminosity functions (PNLF) for the purpose of 

distance determinations (see review by Ciardullo 2003). Approximately 5000 XPN in more 

than 40 galaxies have been identified to date (Ford et al. 2002). 

Currently the only way to measure individual abundances from old or intermediate age stars 

in galaxies more distant than the Magellanic Clouds is through the emission line spectra of 

extragalactic planetary nebulae (Walsh et al. 2000). This approach has some similarities with 

the standard method of measuring abundance gradients from individual H II regions in the 

disk of spiral galaxies (Shaver et al. 1983, Zaritsky et al. 1994). As opposed to H II regions, 

XPN metallicities can be derived in a homogeneous way for galaxies of any Hubble type, and



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on all scales of galactocentric distances. The task of obtaining abundance gradients out to 

large radii, where a low surface-brightness precludes to measure reliable colors or absorption 

line indices, can be addressed with XPN, providing important constraints for galactic 

evolution models (Worthey 1999). Moreover, since radial velocities of XPN are measurable 

out to several effective radii, they are potentially useful for probing the gravitational potential 

of galaxies (Méndez et al. 2001, Romanowsky et al. 2003), and for tracing merger events (Hui 

et al. 1995, Durrell et al. 2003, Merrett et al. 2003). 

Recently, XPN and an H II region have been detected in the intracluster space of the Virgo 

cluster, giving an excellent opportunity to study the properties of this unique stellar 

population and, potentially, their star formation history and metallicity (Arnaboldi et al. 2002, 

Gerhard et al. 2002, Feldmeier et al. 2003). 

Several authors have pioneered spectroscopic observations of individual XPN in nearby 

galaxies and derived abundances from the observed emission line intensities, e.g. Jacoby & 

Ciardullo 1999 (M31), Richer et al. 1999 (M31, M32), Walsh et al. 1999 (NGC5128), 

Magrini et al. 2003 (M33). A wealth of data exists for Magellanic Cloud objects which are an 

order of magnitude closer and therefore much easier to observe than those in M31 and other 

more distant galaxies. The LMC study of Dopita et al. 1987 has demonstrated the potential of 

XPN to investigate the chemical evolution of stellar populations. 

Unfortunately, the study of XPN near the center of the more distant galaxies is complicated 

by source confusion, either from the continuum light of unresolved stars with a small angular 

separation from the target, or from the emission line spectra of H II regions and diffuse 

nebulosities of the interstellar medium (ISM), or from both components at the same time. In 

fact, these first studies were all significantly affected by background contamination, which is 

a severe problem in particular for the faint nebular diagnostic lines. Roth et al. 2003 presented 

a methodological study of selected XPN in the bulge of M31, showing that 3D spectroscopy 

is an ideal technique to overcome these difficulties. 

Objectives: 

The immediate goal is to perform deep 3D spectrophotometry of XPN in early and late type 

galaxies, from relatively nearby objects (~3 Mpc) out to Virgo. From the comparison of the 

observed emission line intensities with ionization models, it will be possible to derive nebular 

abundances of He, N,O, Ne, S, Ar, and to constrain central star properties (effective 

temperature, mass). Radial velocities are easily measurable from the bright [O III] λ5007 line. 

The final objective is to provide independent kinematic and abundance information for the 

intermediate/old parent populations, complementing photometric and integrated light stellar 

population studies, and new data coming from quantitative spectroscopy of individually 

resolved, massive stars. As an asset, the analysis will map the extinction over the face of the 

galaxies under study. 

Feasability: 

Planetary nebulae span a wide range of apparent brightness in their prominent [O III] 

emission line λ5007. Narrow-band imaging observations of numerous galaxies, beginning 

with the pioneering work of Jacoby 1989 and Ciardullo et al. 1989, have established an 

invariable shape of the PNLF, with a cutoff magnitude of M5007 = –4.5, where m 5007 = -2.5



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log F(λ5007) –13.74. For example, Soffner et al. (1996) discovered 34 PNe in NGC300, 

using the NTT + SUSI (2.2x2.2 arcmin 2 FOV) under less than ideal observing conditions 

(seeing 2.0”-0.9”), and with exposure times of 3600s per field, for three fields centered on the 

nucleus. The magnitude range of these objects is m 5007 = 22.85–27.08, corresponding to flux 

levels of 2.4x10 -15 – 5x10 -17 erg/cm 2 /sec, respectively. 

First of all, using the light collecting power of the VLT and excellent seeing conditions, the 

detection limit with MUSE will be 2 magnitudes fainter than this. Due to the 10-fold smaller 

bandwidth of a MUSE exposure, compared with a the typical filter bandwidth of PNLF 

observations, the former has a significant advantage over the latter in terms of backgroundlimited 

exposures in high surface brightness regions near the nucleus, where normally 

narrow-band imaging data tend to become incomplete. Using 3D spectroscopy as an 

extremely narrow-bandwidth filter, MUSE will detect hundreds of PNe two orders of 

magnitudes further down the PNLF, compared to the earlier NTT observations. 

Secondly, spectrophotometry of the PN emission line spectrum will be feasible for the entire 

range of magnitudes m 5007 = 22.85 … 27.08, even for the faint diagnostic lines, whose line 

intensities are typically no brighter than 10 -2 I([O III]). 

Tab. 4-3 S/N estimates for PNe at [O III] 5007 Å 

Flux 

(erg/cm 2 /sec) 

Exposure [sec] S/N 

5x10 -17 4x 3600 100 

5x10 -18 4x 3600 21 

5x10 -19 4x 3600 3 

The unique contribution of MUSE in all these case is the combination of area and an ability to 

exploit enhanced seeing. All these objects are `crowded’ under normal conditions, making 

impossible a quantitative study of the astrophysics. For example, the astrophysics of our 

Galactic nucleus was unknown until the high resolution studies by Genzel’s group. With 

supernova progenitors, only one sound precursor identification was available until very 

recently. Similar advances may confidently be expected in all the fields of star formation, 

high-mass stars, mass loss, dense dynamical systems, resolved emission line sources (PNae, 

SNae, jets, etc) and so on, when data become available from MUSE. 

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5. Solar system 



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There are a number of exciting scientific questions in the field of planetary sciences to which 

MUSE will significantly contribute. We consider 3 major distinct categories: 

• Investigation of the planetary surfaces of the Galilean satellites and of Titan's 

atmosphere and surface, 

• Study and mapping of the optical and mineralogical surface heterogeneities of the 

small bodies of the solar system (earth-grazing and main belt asteroids, comets), 

• Monitoring of the temporal evolution (e.g. seasonal effects) of the atmospheres of the 

giant planets and their dynamics, with special emphasis on the observation of the 

atmospheres of Neptune and Uranus. 

5.2. Galilean Satellites and Titan surfaces 

As recently explored by the Galileo mission and AO-assisted ground-based telescopes, the 

surfaces of the Galilean satellites are undergoing major resurfacing processes as the result of 

different geological processes. Intensive volcanic resurfacing on a planetary scale is occurring 

on Io, as evidenced by the significant regional surface changes observed in the hot spots areas 

between the Voyager and Galileo missions (Spencer et al., 1996; McEwen et al., 1998; 

McEwen et al., 2000; Douté et al., 2001; Geissler et al., 2001; Marchis et al., 2002). It can be 

traced and documented spectroscopically in the visible-near infrared range, with special 

emphasis on the surface mixing related to the occurrence of SO 2 species. MUSE could carry 

out observations aimed at: 

• Providing a global spectroscopic imaging coverage, including the edge of the 1 micron 

domain which is sensitive to the presence of mafic silicates, thus determining the 

proportion of the surface covered by exposed silicate magma; 

• Monitoring through time major surface changes occurring on Io beyond 2011 (i.e. 5 to 

10 years after the Galileo mission) and consequently to constrain the amount of 

volcanic resurfacing at planetary scale (see Fig. 5-1). 

“High angular resolution provided by adaptive optics systems on 8 m class telescopes is a 

promising tool for monitoring the volcanic activity of Io from the ground with a spatial 

resolution better than the global Galileo/NIMS observations. With the end of the Galileo 

mission, the future monitoring of Io’s volcanism lies in the hands of terrestrial observers with 

the ability to make spectroscopic AO observations" (Marchis et al., 2002). MUSE will play a 

key role in fulfilling this responsibility of ground-based observers to monitor activity on IO, 

providing unique access to high spatial resolution spectroscopic imaging at visible and nearinfrared 

wavelengths in the post-Galileo mission era.



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Fig 5-1 - Taken from Marchis et al., 2002). It demonstrates what could be monitored at Io by MUSE in 

the visible-nIR domain. (a) Jupiter-facing hemisphere observed with the Keck AO system. The basicprocessed 

images from 20 February 2001 (first row) are displayed. The second row corresponds to the 

same images after applying the MISTRAL deconvolution process. Albedo features, similar to the 20-kmresolution 

reconstructed GALILEO/SSI image (right column) are easily detected. The last row shows the 

22 February 2001 images, which are dominated by the presence of the Surt outburst. (b) Observations 

from 19 February 2001. Two hot spots, corresponding to Tvashtar (North) and Amirani, are clearly 

detected in the H and K bands. Note the bad quality of the J-band image after deconvolution, due to the 

poor seeing condition of this observing night.



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For Europa, Ganymede and Callisto, more subtle resurfacing processes are now also 

recognized and an intermittent systematic monitoring of the optical surface properties should 

also be made. All these objects have an apparent diameter ranging between 1 and 2", and are 

therefore perfectly suited to the MUSE-HR field of view, allowing efficient surveying and 

monitoring of their surfaces. 

MUSE could also extend the spectro-imaging monitoring of Titan's atmosphere to be 

conducted by Cassini during the period 2004-2007 and which is also of high scientific value 

and benefit. This would significantly contribute to the understanding of the general 

atmospheric circulation for which the present modelling efforts are only considering a 

standard atmospheric photochemistry profile, with no lateral or temporal variations. At 

present, such modelling simulates latitudinal temperature contrasts in the stratosphere that are 

significantly weaker than those observed by Voyager 1, and it may be partly due to the 

absence of the spatial and temporal variations of the abundances of molecular species and 

haze. 

By considering different spectral windows within the visible to near-infrared wavelength 

range of a single MUSE exposure, it will be possible to probe down into the atmosphere of 

Titan, and measure the mesoscale lateral and vertical structures and characteristics. Methane 

and absorption bands have been documented by earth-based spectroscopic observations in the 

4900-5500; 6000-6600; 8700-9300 Å domains (Moreno et al., 1991, Coustenis et al., 1995; 

Combes et al., 1997) and the comparison of spectral images acquired by the WFPC camera of 

Hubble Space Telescope at 4400, 5500 and 8890 Å with Voyager images indicate 

atmospheric seasonal changes (Caldwell et al., 1992; Smith et al., 1996). Temporally-resolved 

monitoring of Titan would be well-suited to MUSE's high-resolution capabilities, with the 

diameter of Titan (including its atmosphere) being in the range of 1.0-1.2", resulting in a 

predicted effective spatial resolution of 140-150 km per lens. 

5.3. Surface heterogeneities of the small bodies 

For the study of comets and asteroids in the solar system, MUSE will produce several 

major breakthroughs. For the study of comets, there will be: 

- Study at high spatial resolution (3.5km/pixel for a comet at 0.2 a.u.) of morphology of 

the internal coma, within 300-500 km around the cometary nucleus, of the 

relationships with the activity of the nucleus (e.g., distribution of active zones and the 

rotational parameters of the nucleus). 

- Spectroscopic study of the cometary dust: spectral variations as a function of the 

distance to the nucleus. Relationships with the ejected gases. 

- In a few cases (closest approaches), it might be possible to extract the contribution of 

the nucleus to the reflected light by the centre of the coma and consequently to 

estimate the diameter of the nucleus. 

and for asteroids: 

- Despite the recent extension toward the infrared, the general taxonomic classes of 

asteroids are based on colour photometry in the 0.3-1.1 micron domain (see summary 

of Tholen and Barucci, 1989).



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- The asteroids orbiting the sun within the main belt have perihelion distances ranging 

between 1.6 and 3.3 a.u.. The possible spatial resolutions, assuming an angular 

resolution of 0.025", would thus range from 10 to 40 km/pixel. 

- For the largest objects (more than 10), it permits the mapping of the optical and 

mineralogical surface heterogeneities and consequently provides insight into their 

accretional and subsequent geochemical differentiation history. As an example, taking 

advantage of its rotation, MUSE could produce a global map of Ceres (diameter: 1025 

km). 

5.4. Temporal changes in Jupiter, Saturn, Uranus and 

Neptune 

The predicted spatial resolution delivered by MUSE in the high-resolution mode 

(corresponding to a 0.025" angular resolution) translates into effective physical scales as 

follows: 75 km/pixel at the distance of Jupiter, 150 km at Saturn, 300 km at Uranus and 500 

km at Neptune. The latter two objects represent the best candidates for both a synoptic survey 

and a truly new return in terms of scientific knowledge. Furthermore, their apparent diameters 

of about 4" and 2.3", respectively, make them ideal for global monitoring given the field of 

view of MUSE in its high spatial resolution mode. For Jupiter and Saturn, a global monitoring 

would be more time-consuming, requiring mosaics of several high-resolution fields. Instead, 

it may be more practical for these objects to target regional areas of interest (e.g. the red spot, 

polar regions, etc.). 

The key contribution of MUSE would be to monitor through time the mesoscale changes in 

the atmospheric patterns with the possibility of probing the 3-D atmospheric structure by 

examining different spectral windows along the extensive MUSE wavelength range, 

depending on the considered spectroscopic absorptions related to gaseous species such as: 

CO, C2H2, NH3, HC3N, CH4, etc. (e.g., Tomasko et al., 1984; West et al., 1986). For 

instance, in the case of the atmospheres of Uranus and Neptune, photons in the 4900-6600 Å 

wavelength range penetrate to the deep convectively mixed atmospheric layers, giving 

information on the deep methane abundance and aerosols properties (Moreno, et al., 1986). 

References: 

Caldwell, J., C.C., Cunningham et al. (1992). Titan: Evidence for seasonal change- A 

comparison of Hubble Space Telescope and Voyager images, Icarus, 96, 1-9. 

Combes, M., L. Vapillon, E. Gendron, A. Coustenis, O. Lai, R. Wittemberg, and R. 

Sirdey.(1997). Spatially Resolved Images of Titan by Means of Adaptive Optics, Icarus, 129, 

482-497 

Coustenis,A., E. Lellouch, J. P. Maillard, and C. P. McKay. (1995).Titan's surface: 

composition and variability from the near-infrared albedo, Icarus, 118, 87-104. 

Doute, S., B. Schmitt, R. Lopes-Gautier, R. Carlson, L. Soderblom, J. Shirley and the Galileo 

NIMS Team 2001, Mapping SO2 frost on Io by the modeling of NIMS hyperspectral images. 

Icarus 149, 107–132. 

Geissler, P., A. McEwen, C. Phillips, D. Simonelli, R.M.C. Lopes, and S. Douté (2001). 

Galileo Imaging of SO2 frosts on Io, J.geophys. Res., 106, E12, 33253-266.



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Marchis,F.,I., de Pater, A.G. Davies, H.G. Roe, T. Fusco, D. Le Mignant, P. Descamps, B.A. 

Macintosh, and R. Prangé 2002). High-resolution Keck Adaptive Optics Imaging of Violent 

Volcanic Activity on Io, Icarus, 160, 124-131. 

McEwen, A. S., L. Keszthelyi, P. Geissler, D. P. Simonelli, M. H. Carr, T. V., Johnson, K. P. 

Klaasen, H. H. Breneman, T. J. Jones, J. M. Kaufman, K. P., Magee, D. A. Senske, M. J. S. 

Belton, and G. Schubert (1998). Active volcanism on Io as seen by Galileo SSI. Icarus 135, 

181–219. 

McEwen, A. S., M. J. S. Belton, H. H. Breneman, S. A. Fagents, P. Geissler, R. Greeley, J.W. 

Head, G. Hoppa,W. L. Jaeger, T. V. Johnson, L. Keszthelyi, K. P. Klaasen, R. Lopes-Gautier, 

K. P. Magee, M. P. Milazzo, J. M. Moore, R. T. Pappalardo, C. B. Phillips, J. Radebaugh, G. 

Schubert, P. Schuster, D. P. Simonelli, R. Sullivan, P. C. Thomas, E. P. Turtle, and D. A. 

Williams (2000). Galileo at Io: Results from high-resolution imaging. Science 288, 1193– 

1198. 

Moreno, F., A., Molina and J.L. Ortiz (1991). CCD spectroscopic observations of saturn, 

Uranus, Neptune, and Titan during the 1990 apparitions, Icarus, 93, 88-95. 

Tholen, D.J. and M.A., Barucci (1989). Asteroid taxonomy, In Asteroids II, Eds. R.P. Binzel, 

T. Gehrels, and M.S. Matthews, 298-315, Univ. Of Arizona Press, Tucson. 

Smith P.H., M. T. Lemmon, R. D. Lorenz, L. A. Sromovsky, J. J. Caldwell, and M. D. Allison 

(1996). Titan's Surface, Revealed by HST Imaging, Icarus, 119, 336—34 

Spencer, J. R., and N. M. Schneider (1996). Io on the eve of the Galileo mission. Ann. Rev. 

Earth Planet Sci., 24, 125-190.



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6. Serendipity 

Astronomy is to a significant degree still driven by unexpected discovery (e.g. dark matter 

and dark energy). These discoveries are often made by pushing the limit of observations with 

the most powerful telescopes and/or opening a new area of the instrumental parameter space. 

MUSE is designed to push the VLT to its limit and to open a new parameter space area in 

sensitivity, spatial resolution, field of view and simultaneous spectral coverage. We are 

convinced that it fulfils all the required conditions to have a large potential of discoveries: 

• It will be the first spectrograph that could blindly observe a large volume of space (10 4 

Mpc in one deep field), without any imaging preselection. 

• It will be the first optical AO assisted IFU working at improved spatial resolution (0.3 

arcsec FWHM) in most atmospheric conditions (70% probability) with a large sky 

coverage (better than 60% at galactic poles). 

• It will be the first spectrograph optimized to work with very long integration time (80 

hours) and to reach extremely faint emission line detection (3.9 10 -19 erg.s -1 .cm -2 ). 

MUSE will thus be able to discover objects 

that have measurable emission lines, but 

with a continuum that is too faint to be 

detected in broad-band imaging. For 

example, the deepest broad-band imaging 

available today is the HST Ultra Deep Field 

(UDF) with I AB 5.5) will have a 

continuum bright enough to be detected in 

Figure 6-1: Crampton et al. 2002, (astroph/0201344), 

Serendipitous discovery of a case of 

the UDF. MUSE is also the only instrument 

capable of detecting faint diffuse ionized galaxy-galaxy lensing. This geometry allows an 

gas, like extended halos or filaments. accurate estimate of the mass distribution of the 

Finally, objects with unusual spectral lensing galaxy. 

features should also be detected by MUSE, 

whatever their broad band magnitude and colours are. 

In order to illustrate the enormous scientific multiplex gain from obtaining complete data 

cubes over large areas of the sky with wide wavelength (and hence redshift) coverage, we 

show in Figure 6-1 an example of galaxy-galaxy lensing discovered during the course of the 

Canada-France Redshift Survey (CFRS). In the case of CFRS, two out of 350 galaxies imaged 

with HST showed such galaxy-galaxy lensing, so we expect a large number of these in a 

MUSE data cube. Galaxy-galaxy lenses are a powerful means of measuring the gravitational 

potential of the lensing galaxy. 

Serendipity is by essence not easy to quantify and therefore extremely hard to make a science 

case for. Despite this, it is very likely that the most memorable results from MUSE will come 

from discoveries that are currently not anticipated in this proposal.

7. Instrument requirements 



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Instrument requirements are summarized in Table 1. For each science subject we have 

identified the following requirements: 

• Field of view (FOV) importance (compared to a FOV reduced by a factor 2), quoted 

from 1(less important) to 3 (critical) 

• AO need, quoted from 0 to 3, with the following meaning: 

o 0 : no AO needed 

o 1 : AO is preferred but not absolutely requested 

o 2 : AO is mandatory to achieve full science but non-AO observations allow 

partial fulfilment of the science goals 

o 3 : AO is critical to science goals 

• Wavelength bandpass. Importance of the bandpass 8 is quoted from 1 (less important) 

to 3 (very important). Bandpass are defined according to MUSE spectral range: 0.465- 

0.6 µm (B-V), 0.6-0.8 µm (V-I), 0.8-0.96 µm (I-z) 

• Importance of R=3000 spectral resolution (compared to R=1500), quoted from 1 (not 

important) to 3 (critical) 

Science Subject 9 

FOV AO Wavelength Spec. 

Mode 

area 

B-V V-I I-z Resol. 

Gal. Form SF WFM 3 0 2 2 2 2 

Gal. Form MDF WFM 3 1 2 2 3 2 

Gal. Form DF WFM 3 3 2 2 3 3 

Gal. Form UDF WFM 3 3 2 2 3 3 

Near Gal. SBH NFM 1 3 2 1 3 2 

Near Gal Kin. & Stel. Gal. WFM 2 2 3 2 1 2 

Near Gal Inter. Gal. WFM 3 1 2 2 2 2 

Near Gal Star. Form. WFM 2 2 2 3 1 2 

Stars YSO WFM 2 2 1 3 1 3 

Stars YSO NFM 2 3 1 3 1 3 

Stars LMC WFM 3 0 3 2 2 3 

Stars Mass. Stars/XPN WFM 3 2 3 2 1 3 

Stars Globular cluster WFM 2 3 3 2 2 3 

Solar Syst. Planets Atm. NFM 3 3 2 3 1 1 

Solar Syst. Satellites/Aster. NFM 3 3 2 2 2 1 

Table 1: Main instrument requirement relative to science goals 

8 A bandpass is defined as less important if a decrease of 50% in throughput and/or image quality does not affect 

science feasibility. 

9 SF, MDF, DF and UDF refer to shallow, medium deep, deep and ultra-deep fields. SBH is the supermassive 

black hole study, Kin & Stel. Gal. the kinematics and stellar populations in nearby galaxies, Inter. Gal. the 

interacting galaxies and Star. Form. the star formation. YSO is the early stage of stellar evolution, LMC the 

Large Magellanic Cloud, Mass. Stars/XPN the extragalactic massive stars and planetary nebulae. Planets Atm. 

are the planetary atmosphere and Aster. the asteroids.



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Note that AO is by definition critical to NFM, whatever the science goals. We now provide 

some more detailed comments for each science area. 

7.1. "Formation of galaxies" science case 

For surveys, the field of view is obviously critical. The AO requirements are related to the 

depth of the observations. While AO is not necessary for the shallow field (SF) and not 

mandatory for the medium deep field (MDF), it is critical to deep and ultra-deep fields in 

view of the limiting flux and spatial resolution goals. The whole MUSE wavelength range is 

important: for high z galaxies (z>5), the red spectral range is critical, but for the cosmic web 

measurements (z~3) and spatially resolved spectroscopy (z



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8. Competitiveness 


MUSE, as any second generation VLT instrument, should be able to maintain and expand the 

VLT world-wide competitiveness in 2010+. As such, it should not only be superior to existing 

instruments in terms of performance, but should also extend the scientific capabilities of the 

observatory into unique areas. In this section we try to assess its competitiveness. 

Competitiveness is generally a difficult issue to discuss, and even more difficult to properly 

quantify. The most valid approach is to compare MUSE with other instruments or facilities in 

view of each of the science goals, such as the comparisons already made and discussed 

throughout the various science case presentations. The key points to emphasize from these 

comparisons are that the MUSE sensitivity performance is around two orders of magnitude 

better than narrow-band imaging; the volume-depth which can be surveyed with MUSE is 

unprecedented; and its ability to provide both high-spatial resolution spectroscopic 

information across a wide field of view, or to get diffraction-limited resolution at visible 

wavelengths over a smaller area, are unparalleled by any other instrument, existing or 

planned. The MUSE science team believes that the large majority of the presented science 

cases are unique to MUSE, either because they are simply not feasible with other instruments 

(e.g. detection of high-z faint Ly α emitters that are not detectable in broad band imaging, even 

with HST), or because it would take in excess of 10–100 times longer to perform the same 

survey with other facilities, and so are simply not tenable. For example, as discussed in 

section 4.4, the combination of large field-of-view with seeing-limited spatial sampling makes 

MUSE WFM an unrivalled tool for background-limited spectroscopy of resolved stellar 

populations in nearby galaxies: 260× more efficient than FLAMES, and 210× more efficient 

than the GMOS-IFU. 

It is also possible to compare MUSE with other instruments in term of generic performance. 

This approach is also important, not only because in seven years, science goals may have 

evolved, but also because the science team has its own bias and cannot represent all interests 

of the entire ESO community. MUSE is an IFU and should therefore be compared to others 

IFUs that share some of its characteristics: wide-field of view, high spatial resolution, large 

simultaneous spectral range and medium spectral resolution. Here we compare the two modes 

of MUSE with existing or planned wide-field or high spatial resolution IFUs.



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8.2. Wide field IFU 

Among the very few existing wide-field IFUs that could compete with MUSE, VIMOS has 

the largest field of view with 0.8 arcmin². This large field of view, however, is only obtained 

at R~250: a spectral resolution far too low to be competitive for line emission detection and 

analysis, or for accurate sky-subtraction in the near-infrared. We have therefore restricted the 

comparison to the high spectral resolution mode of VIMOS. To quantify the performance 

comparison between the two instruments, we present in the following table some possible 

figures of merit. We have also included SAURON, another wide field IFU operating at the 

WHT, as a reference in this table. 

SAURON VIMOS MUSE 

MUSE/VIMOS 

LR mode HR-Blue WFM 

Telescope area (m²) 13.1 51.7 51.7 1.0 

Ω (arcsec²) 41x33 27x27 60x60 4.9 

Grasp (m².arcsec²) 17724 37689 186120 4.9 

Throughput 0.2 0.066 0.24 3.6 

Etendue 3545 2487 33502 17.6 

Resolving power 1200 2500 3000 1.2 

Nb of resolved spectral elts 129 920 2048 2.3 

Spectral power (x 10 6 ) 0.16 2.3 6.1 2.6 

Nb of resolved spatial elts 1,431 1,600 40,000 13.5 

Spatial power (x 10 6 ) 1.9 1.2 144.0 120.0 

3D power (x 10 12 ) 0.3 2.8 878.4 312.0 

Table 8-1: Comparison of wide field IFUS 

Note on the table parameters 

• Grasp is the field of view times the telescope area 

• Etendue is the field of view times the throughput 

• Spectral power is the number of resolved spectral elements times the spectral 

resolution 

• Spatial power is the number of resolved spatial elements times the field of view. In the 

case of MUSE, a spatial resolution of 0.3 arcsec is assumed. In the case of VIMOS, 

this is simply the number of spaxels. 

• 3D power is the product of spatial and spectral power 

One interesting number is the Etendue, which is directly linked to the speed for performing a 

survey. Because of its large field of view and better throughput, MUSE is more than ten times 

faster than VIMOS. Another key number is the 3D power, a number that measures the 

datacube information content that could be explored by the instrument. In this respect, MUSE 

is more than 2 orders of magnitude better than VIMOS. 

These figures of merit are just an indicator to facilitate a quantifiable comparison. The 

capability of an instrument to conduct very faint-object science depends on a number of



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parameters, which are generally not easy to quantify, such as the accuracy of sky subtraction, 

PSF stability, minimization of diffuse light, etc. However, optimizing such key aspects of the 

instrument performance forms the very basis upon which MUSE has been designed and shall 

be built, and we are confident that this will ultimately ensure the achievement of our aims. 

8.3. High spatial resolution IFU 

In contrast to wide-field IFUs, which are not numerous, there is a growing number of AOassisted 

IFUs in development or in operation. In the following table we show the main 

competitors (operating or planned) for 8m telescopes. 

Instrument GNIRS+IFU NIFS OSIRIS SINFONI MUSE 

Telescope Gemini S Gemini N Keck VLT VLT 

Multiplex type Slicer IFU Slicer IFU Lenslet IFU Slicer IFU Slicer IFU 

No. spatial elements 620 2000 1000 1000 90,000 

Spatial sampling, 

arcsec/pixel 

0.07x0.07 0.04x0.10 0.02 

0.05 

0.10 

FOV (arcsec) 2.2x1.5 3.0x2.9 1.28x0.32 

3.20x0.80 

6.40x1.60 

0.025 

0.10 

0.25 

0.8x0.8 

3.2x3.2 

8x8 

0.025 

7.5x7.5 

Spectral elements 1000 2000 1300 2000 4000 

Spectral resolutions 1800, 6000 5300 3800 4000 3000 

Wavelength range 0.9-2.5 µm 0.9-2.5 µm 1-2.5 µm 1-2.5 µm 0.46-0.93 µm 

Detector 1kx1k InSb 2k x 2k 

HgCdTe 

2k x 2k 2k x 2k 24x4kx4k 

CCD 

Date comm. 2003 (NS) 2005 2004 2004 2011 

Table 8-2: Existing or planned AO assisted IFU on 8m class telescopes 

In the following we have focused our comparison with SINFONI which is available at VLT, 

but most of what will follow is also be valid for the other instruments. We limit our 

comparison to the 25 milli-arcsec sampling of SINFONI. 

One can then perform the same computation as in previous subsection. This gives a gain of 90 

in Etendue 10 and 135 in 3D power for MUSE with respect to SINFONI. But the key 

parameter is the spatial resolution given by the AO system. It is well known that AO 

performance is much better in the infrared than in the optical. The SINFONI predicted 

performances are quite good, with 50% Strehl ratio in the K band. However, this performance 

is obtained only with a bright, natural on-axis guide star (V



Issue: 1.3 

Date: 04/02/2004 

Page: 100/100 

However it shows that MUSE NFM should achieve similar or better spatial performance than 

SINFONI, with the advantage of a much larger field of view. 

Another parameter is the sensitivity. For example, the published SINFONI surface brightness 

sensitivity gives K=12.7 magnitude.arcsec -2 , for a one hour exposure, whereas MUSE ETC 

gives I AB =17 arcsec -2 in the same conditions. It must also be emphasized that, with their 

comparable spatial resolutions and distinct wavelength coverage, MUSE NFM and SINFONI 

are remarkably complementary. 

Finally, with similar or better spatial resolution, much larger field of view and better 

sensitivity, MUSE should have the performance gain that one would expect for an instrument 

coming into operation at VLT seven years later.

Exposure Time 

Calculator and 

Performance Analysis 

Written by : R. Bacon 



Issue : 1.3 

Date : 28/01/04 

File : 

muse_etc.doc 

Distribution : ESO 

History: 

• 1.0 – 22/12/03 – Initial version with inputs from Simon Morris and Ian Parry 

• 1.1 – 09/01/04 – Comments by Richard Mc Dermid 

• 1.2 – 15/01/04 – Comments by Luca Pasquini 

• 1.3- 28/01/04 – Final Phase A release

Title: ETC and performance analysis 


Issue: 1.3 

Date: 28/01/04 

Page: 2/14 

1. Introduction ........................................................................................................................ 3 

2. Documents.......................................................................................................................... 3 

2.1. Applicable documents ................................................................................................ 3 

2.2. Reference documents ................................................................................................. 3 

3. Acronyms ........................................................................................................................... 3 

4. Assumptions....................................................................................................................... 5 

5. Results ................................................................................................................................ 6 

5.1. Wide-field mode......................................................................................................... 6 

Extended source .................................................................................................................6 

Unresolved source.............................................................................................................. 7 

5.2. Narrow field mode ..................................................................................................... 8 

Extended source .................................................................................................................8 

Unresolved source.............................................................................................................. 8 

6. Analysis.............................................................................................................................. 9 

6.1. Wavelength dependency ............................................................................................ 9 

6.2. Improvement due to AO............................................................................................. 9 

6.3. Noise regime ............................................................................................................ 10 

Wide field mode............................................................................................................... 10 

Narrow field mode ........................................................................................................... 10 

6.4. Centering star ........................................................................................................... 10 

6.5. Other models of throughput ..................................................................................... 10 

7. Limitations ....................................................................................................................... 12 

8. Plan for phases B & C...................................................................................................... 12 

ANNEX – ETC mathcad sheet................................................................................................. 13



Issue: 1.3 

Date: 28/01/04 

Page: 3/14 


This document describes the MUSE exposure time calculator developed during phase A and 

discusses the instrument performances. Limiting flux and magnitude quoted in the Science 

Case document refer to these ETC results. 

2. Documents 

2.1. Applicable documents 

AD1 MUSE Top Instrumental Parameters 

AD2 MUSE AO analysis report 

AD3 MUSE System analysis and budgets 

AD4 MUSE Instrument detector system specification 


VLT-TRE-ESO-14675-2951 

MUSE-MEM-TEC-031 


2.2. Reference documents 

RD1 ESO ETC web pages 

RD2 A flux calibrated, high resolution atlas of optical 

emission sky from UVES 

RD4 MUSE Science Case 

Hanuschik R.W, 2003, A&A, 407, 

1157 


3. Acronyms 

AD 

AO 

CCD 

ESO 

ETC 

FoV 

FWHM 

INM 

MUSE 

NA 

NFM 

PSF 

R 

RD 



Charge-Coupled Device 


Exposure Time Calculator 

Field of View 

Full Width Half Maximum 

Instrument Numerical Model 





Spectral Resolving Power 

Reference Document

S/N 

TBC 

TBD 

VLT 

WFM 



Issue: 1.3 

Date: 28/01/04 

Page: 4/14 

Signal over noise 

To Be Confirmed 

To Be Defined 


Wide Field Mode



Issue: 1.3 

Date: 28/01/04 

Page: 5/14 

4. Assumptions 

Mathematics and detail computation can be found in the appendix. All important assumptions 

and input parameters are shown in following table. 

Item Assumptions Remark 

Sky brightness Continuum only (outside OH emission lines), 

reference RD2, moon aged of 5 days 

S/N estimate is only valid 

outside OH lines (see RD1 

for an estimate of the free 

OH fraction of spectral 

range) 

Atmospheric Paranal extinction (reference RD1) 

An airmass of 1 is assumed 

extinction 

Telescope 485425.1 cm² (reference RD1) 

effective area 

Instrument 

throughput in WF 

Reference AD3 

Typical curve with adaptive 

secondary 

mode 

Instrument Provisionally taken equal to WF mode Overestimated by 5% 

throughput in HR 

mode 

CCD dark current 3 electrons/hour (reference AD4) Typical value 

CCD readout 4 electrons (reference AD4) Fairchild 4k x 4k 

Spatial PSF 4 cases considered: poor (1.1 arcsec, 70%-tile) Current Paranal statistics 

and good (0.65 arcsec, 30%-tile) seeing (5/99-8/02) 

conditions, with and without AO. Simulations 

take into account PSF variation with wavelength 

(VLT-TRE-ESO-14675-2951). All PSFs are 

convolved with MUSE image quality, assumed to 

be 0.254 arcsec FWHM (AD1) 

Number 

summed 

pixels (1) 

of 

spatial 

3x3 pixels (0.6x0.6 arcsec²) in good seeing 

conditions and 4x4 pixels (0.8x0.8 arcsec²) in 

poor seeing conditions, both for AO and non AO 

observations 

For spatially unresolved 

source. Object flux fraction 

recovered is 40-60%. 

Spectral PSF Gaussian shape of 2 pixels FWHM 

Number of 3 pixels for emission line source, 1 or 10 pixels 92% fraction of flux 

summed spectral for respectively full and low spectral resolution enclosed in case of 

pixels (2) continuum 

emission line source 

Exposure time 1 hour Limited by cosmic ray 

impacts 

Number of 80 for deep field and 1 for shallow field 

exposure 

Limiting S/N 5 for 1 resolution element A resolution element being 

defined in (1) and (2)

5. Results 



Issue: 1.3 

Date: 28/01/04 

Page: 6/14 

5.1. Wide-field mode 

Extended source 

The following two tables give the instrument performance at full spectral resolution in case of 

a spatially extended source with a flat continuum spectral distribution (AB surface magnitude) 

or an unresolved emission line (line flux by arcsec² in 10 -19 erg.s -1 .cm -2 units). Two cases are 

considered, a single exposure of 1 hour (shallow field) and a series of 80 exposures of 1 hour 

(deep field). Values are given at central wavelength of photometric band, except for B’ and z’ 

which are set to the limits of the MUSE spectral range. 

Shallow Field Observations (1h) – Extended source 

λ (µm) Band AB arcsec -2 Line flux arcsec -2 

0.465 B’ 20.5 595.6 

0.55 V 21.2 221.3 

0.64 R 21.3 154.1 

0.79 I 20.8 153.4 

0.93 z’ 20.0 221.9 

Deep Field Observations (80x1h) – Extended source 


0.465 B’ 23.28 53.8 

0.55 V 23.87 21.4 

0.64 R 23.93 15.0 

0.79 I 23.48 14.9 

0.93 z’ 22.78 20.4



Issue: 1.3 

Date: 28/01/04 

Page: 7/14 

Unresolved source 

The next two tables display the limiting magnitude and line emission flux (in 10 -19 erg.s -1 .cm -2 

units) in the case of spatially unresolved object. Various conditions have been explored for 

the spatial PSF: seeing limited observations (non AO column) and AO observation, each in 

two atmospheric conditions: good (seeing of 0.65 arcsec) and poor (seeing of 1.1 arcsec). To 

retrieve a significant fraction of the object flux, we have summed in an area of 0.6x0.6 arcsec² 

in the case of good seeing conditions and 0.8x0.8 arcsec² in the case of poor seeing. A low 

spectral resolution limiting magnitude at a tenth of the nominal spectral resolution is also 

given (obtained by summation of ten spectral pixels and assuming a flat continuum spectral 

distribution). Shallow (1h) and deep (80x1h) exposures are presented. 

Shallow Field Observations (1h) – Unresolved Source 

λ (µm) Band AB full R AB R/10 Line Flux 

Atm. cond Poor Good Poor Good Poor Good 

0.465 B’ 

Non AO 21.7 22.1 23.0 23.5 223.3 144.4 

AO 21.8 22.4 23.1 23.7 199.0 111.2 

0.55 V 

Non AO 22.4 22.8 23.7 24.2 84.2 53.5 

AO 22.7 23.1 24.0 24.5 64.1 40.9 

0.64 R 

Non AO 22.5 22.9 23.8 24.3 56.4 36.3 

AO 22.8 23.2 24.1 24.6 42.5 27.4 

0.79 I 

Non AO 22.1 22.6 23.4 23.9 52.2 33.4 

AO 22.4 22.9 23.7 24.2 38.7 25.3 

0.93 z’ 

Non AO 21.4 21.9 22.8 23.2 68.7 44.8 

AO 21.8 22.2 23.1 23.5 49.7 33.7 

Deep Field Observations (80x1h) – Unresolved Source 



0.465 B’ 

Non AO 24.1 24.6 25.4 25.9 23.6 15.0 

AO 24.3 24.9 25.5 26.2 21.1 11.6 

0.55 V 

Non AO 24.8 25.3 26.1 26.6 9.1 5.7 

AO 25.1 25.6 26.4 26.9 6.9 4.4 

0.64 R 

Non AO 24.9 25.4 26.2 26.7 6.1 3.9 

AO 25.2 25.7 26.5 27.0 4.6 2.9 

0.79 I 

Non AO 24.5 25.0 25.8 26.3 5.6 3.6 

AO 24.9 25.3 26.1 26.6 4.2 2.7 

0.93 z’ 

Non AO 23.9 24.4 25.2 25.6 7.3 4.7 

AO 24.3 24.7 25.5 26.0 5.3 3.5

5.2. Narrow field mode 



Issue: 1.3 

Date: 28/01/04 

Page: 8/14 

Extended source 

The following table gives the instrument performance at full spectral resolution in case of a 

spatially extended source with a flat continuum spectral distribution (AB surface magnitude) 

or an unresolved emission line (line flux by arcsec² in 10 -19 erg.s -1 .cm -2 units). Values are 

given at central wavelength of photometric band, except for B’ and z’ which are set to the 

limits of the MUSE spectral range. 

Typical Observations (1h) – Extended source 


0.465 B’ 16.49 2564.0 

0.55 V 17.21 702.6 

0.64 R 17.35 472.6 

0.79 I 16.95 480.2 

0.93 z’ 15.93 899.3 

Unresolved source 

The next tables display the limiting magnitude and line emission flux (in 10 -19 erg.s -1 .cm -2 

units) in the case of spatially unresolved object. Only AO observation in good seeing (0.65 

arcsec) conditions is considered. To retrieve a significant fraction of the object flux, we have 

summed in an area of 0.075x0.075 arcsec. A low spectral resolution limiting magnitude at a 

tenth of the nominal spectral resolution is also given (obtained by summation of ten spectral 

pixels and assuming a flat continuum spectral distribution). 

Typical Observations (1h) – Unresolved source 

λ (µm) Band AB full R AB R/10 Line flux 

0.465 B’ 21.08 23.06 214.8 

0.55 V 22.00 24.49 42.6 

0.64 R 22.30 24.85 22.8 

0.79 I 22.09 24.61 18.6 

0.93 z’ 21.66 23.75 28.6



Issue: 1.3 

Date: 28/01/04 

Page: 9/14 

6. Analysis 

6.1. Wavelength dependency 

The computed limiting flux is more or less 

constant with wavelength, except in the 

blue where it shows a rapid decrease short 

ward 0.55 µm. This is partly due to the 

lower throughput in the blue (as shown in 

figure 1) plus a small contribution by 

extinction. In the red, the lower throughput 

is balanced by the linear increase of the 

number of photon per second with 

wavelength for a given flux. 

6.2. Improvement due to AO 

The previously listed performances show 

little difference between AO and non AO 

detection: the gain is 0.2-0.3 magnitude in 

continuum and 30% in line flux. This is 

Figure 1: Variation of throughput (dashed line) and limiting 

flux (solid line) with wavelength. 

due to the relatively large area (9 or 16 pixels) used to recover the object flux. On the other 

hand, if we restrict to the central pixel, we have a much larger difference. An example in 

shown in the following table, with 0.8 magnitude gain and 100% gain in flux between AO and 

non AO observations. 

Deep Field Observations (80x1h) – Unresolved Source – Central pixel only 



Non AO 23.5 24.3 24.8 25.6 14.0 6.7 

0.79 I 

AO 24.2 25.1 25.5 26.4 7.5 3.4



Issue: 1.3 

Date: 28/01/04 

Page: 10/14 

6.3. Noise regime 

Wide field mode 

The following table presents the worst case of noise variance distribution computed in the 

case of deep field observation of an unresolved source and in poor seeing conditions (non AO 

mode). 

Typical noise variance distribution in wide field mode 

λ (µm) Band Object Sky Read Noise Dark Current 

0.465 B’ 1.3% 45.7% 44.6% 8.4% 

0.55 V 0.9% 75.2% 20.1% 3.8% 

0.64 R 0.9% 77.2% 18.4% 3.5% 

0.79 I 0.9% 76.0% 19.4% 3.6% 

0.93 z’ 1.2% 53.7% 37.9% 7.1% 

It shows that we are in photon noise regime in most of the wavelength range with less than 

25% of the total variance due to detector noise. It is only at the two extreme wavelengths that 

the detector noise fraction reaches half of the total variance. These results are for a 

conservative value of 4 electron readout detector noise. Dark current is almost negligible. 

Narrow field mode 

Typical noise variance distribution in narrow field mode 

λ (µm) Band Object Sky Read Noise Dark Current 

0.465 B’ 19.4% 3.5% 65.0% 12.2% 

0.55 V 18.5% 11.5% 59.0% 11.1% 

0.64 R 18.3% 12.7% 58.1% 10.9% 

0.79 I 18.4% 12.0% 58.6% 11.0% 

0.93 z’ 19.2% 4.7% 64.0% 12.0% 

As expected in the narrow field mode, the sky contribution is becoming much smaller in the 

25 milliarcsec pixel and we are then in a detector noise regime. An improvement of detector 

read-out noise will have a major impact in this mode. 

6.4. Centering star 

Optimal merging of the 80 individual 1 hour exposure into the final deep field datacube will 

required a star bright enough to allow accurate centering. According to the ETC a star brighter 

than 22.8 R AB magnitude will reach a S/N of 50 after rebinning at low spectral resolution 

(R~30) resolution. Even at this low spectral resolution, the full spectral range is sample with 

20 points and will allow a good estimate of the spatial PSF and its variation with wavelength. 

6.5. Other models of throughput 

In the different throughput models presented in AD1, we have selected the typical curve 

model. Although we think that this model is a realistic goal, it might be considered as difficult

to achieve. To show the importance of 

throughput, we present in the figure 2 

a performance comparison when using 

the worst and best case models. 

As it can be seen, there is a significant 

difference between the worst and 

typical curve. The limiting flux is 

increased by 30% from 3 to 4 

10 -19 erg.s -1 .cm -2 . Although this is not 

negligible, this is still in phase with the 

deep field science case. Limiting 

magnitude difference between the best 

and typical throughput curves is 

especially important in the red, with a 

decrease by a factor 2 at 0.93 µm. 

Having such a throughput (mainly due 

to CCD QE), will be very benefit for 

the deep field science case and it 

should be a goal for phase B study. 



Issue: 1.3 

Date: 28/01/04 

Page: 11/14 

Figure 2: Limiting deep-field flux (bottom figure) in 

10 -19 erg.s -1 .cm -2 for 3 different throughput models: 

typical (solid line), worst case (dashed line) and best 

case (point-dashed line). The corresponding 

throughput curves are shown in the top figure.



Issue: 1.3 

Date: 28/01/04 

Page: 12/14 

7. Limitations 

The ETC does not take into account any possible systematic such as flat field residuals and its 

results should therefore be considered as optimistic. These 2 nd order effects are by definition 

difficult to model in this simple method. We plan to have a more realistic approach using the 

Instrument Numerical Model (INM). Results from the end-to-end model will be compared to 

the ETC results. 

One possible concern is the accuracy of sky subtraction which may be difficult to achieve in 

the absence of a beam switching or nod and shuffle technics 1 . Outside OH lines, the sky 

brightness is around 410 -19 erg.s -1 .cm -2 .Å -1 .pixel -1 and is nearly constant in our spectral range. 

This is 40 times larger than the faintest object we can detect which translate into a few percent 

precision of sky subtraction. Our experience with SAURON deep fields (see Science Case in 

RD4) shows that it is easy to correct a factor 10. The remaining factor 4 improvement should 

be achievable since, unlike SAURON, MUSE will be specifically designed for deep field 

projects and optimized for very long integration. However this is seen as a critical point and 

will be addressed in depth in the next project phases. 

The ETC point-like sensitivity is based on a summation over a spatial area corresponding to 

roughly half the object flux. This is a simplistic method and could be improved using optimal 

summation scheme taking into account the real spatial distribution of signal and noise. We 

plan to estimate the corresponding gain using the INM and simulated deep fields. 

8. Plan for phases B & C 

In the next phases of the project we will maintain the ETC up to date with the instrument 

performances. The detailed design should allow a better estimate of throughput, image quality 

and detector characteristics, and further AO simulations should improve our knowledge of the 

expected spatial PSF. At the end of phase B, the IFU prototype will also give empirical data 

with which to calibrate the ETC. 

The ETC will be made available to the science team, either via a web interface or via portable 

software. To ensure accurate and realistic results, we will extensively compare ETC results 

with full end-to-end simulations using simulated object datacubes, the instrument numerical 

model and data reduction and analysis software. Updated ETC results will be reassas and 

optimize the observation strategy accordingly. 

1 These technics are not applicable to the deep field given that object and sky locations are unknown and 

wavelength dependant.

ANNEX – ETC mathcad sheet 

Roland Bacon 

version 1.0 11-05-03 

Inspired from Simon Morris mathcad and Ian Parry excel ETC 



Issue: 1.3 

Date: 28/01/04 

Page: 13/14 

version 2.0 8-06-03 Take sky value (no OH) from Hanushik paper 

add variation of ensquared energy with wavelength, use updated version 1.1 of 

throughput 

version 3.0 02-10-03 

Refurbish and simplify the presentation 

updated version 2.1 of throughput 

include also bad seeing conditions for AO performances and add effect of MUSE IQE 

on all PSFs 

version 3.1 09-10-03 

Change z = 0.95 µm and B=0.44 µm traditional wavelength to the red and blue limits 

of MUSE (respectively 0.93 µm and 0.465 µm) 

version 3.2 11-11-03 

Add computation of accuracy needed in sky subtraction 

version 3.3 1-12-03 

Updated version 20/11/03 of throughput with VLT adaptative secondary AO system, 

typical curve. Added computation of surface line emission sensitivity.



Issue: 1.3 

Date: 28/01/04 

Page: 14/14 

1. Units and Constant 

_______________________________________________________________________________ 

Velocity of light in vacuum c 

:= 299792458m ⋅ ⋅s − 1 

Angstroem A := 10 − 10 ⋅m 

microns µm:= 

10 − 6 

⋅m 

Planck's constant (h) h 6.626075510 − 34 

:= 

⋅ 

⋅ joule⋅sec 

phot := 1 

deg 

deg 

arcsec := arcmin := 

3600 

60 

elec := 1 

hour := 3600⋅ 

s 

_______________________________________________________________________________ 

back 

2. Define a few useful functions 

b 

⌠ 

⎮ fx ( ) dx 

⌡ 

a 

Mean( f, a, 

b) 

:= 

b − a 

⎛ 

⎞ 

exp⎜ 

−r 2 

a 

⎜ 

2 σ 2 

⌠ 

⎝ ⋅ ⎠ 

⎮ 2 

GAUSS( r, 

σ) 

:= E GAUSS ( a, 

σ) 

:= ⎮ 

2⋅π 

⋅σ 

⎮ 

⌡− 

a 

2 

FWHM GAUSS ( σ) := 2 2⋅ln( 2) 

⋅σ 

GAUSS( x, 

σ) 

dx 

σ GAUSS ( FWHM) 

:= 

FWHM 

( ) 

2 2⋅ln( 2) 

back



Issue: 1.3 

Date: 28/01/04 

Page: 15/14 

3. Photometric System 

3.1 UBVRIz System 

⎛ 

Bessel, 1979, PASP 91, 589 

⎞ 

⎛ ⎞ 

⎜ 

⎟ 

⎜ 

7.1804 

⎟ 

⎟ 

⎜ 7.4425 ⎟ 

⎟ 

⎜ ⎟ 

7.6408 

⎟ 

⎜ ⎟ 

⎟ 

⎜ 7.9115 

⎟⋅µm 

8.1101 

⎟ 

⎜ 

⎟ 

:= 

⎟ 

⎜ ⎟ 

⎟ 

⎜ 8.4989 ⎟ 

⎟ 

⎜ 8.9706 ⎟ 

⎟ 

⎜ ⎟ 

⎟ 

⎜ 9.4367 ⎟ 

⎟ 

⎜ 10.2649⎟ 

⎜ 

⎠ 

⎝ 10.2692⎠ 

0.36 

7.3788 

⎜ 

⎜ 

0.44 

⎜ 0.55 

⎜ 

0.64 

⎜ 

⎜ 0.79 

λ b := ⎜ 0.95 val_Z 

⎜ 

b 

⎜ 1.25 

⎜ 1.65 

2.2 

3.5 

4.8 

⎜ 

⎜ 

⎜ 

⎜ 

⎝ 

Magnitude central wavelengths and zero 

points from ESO web site 

http://www.eso.org/observing/etc/doc/ge 

n/formulaBook/node12.html 

Central wavelengths to be used 

Useful reference wavelength for MUSE 

λ B := 0.465 ⋅µm 

λ V := λ b2 λ R := λ b3 λ I := λ b4 λ z := 0.93 ⋅µm 

⎛ 

λ B 

⎜ 

⎛ "B" 

⎜ λ V ⎟ 

⎜ 

"V" 

⎜ ⎟ 

⎜ 

λ λ MUSE := ⎜ R ⎟ 

Band MUSE := ⎜ "R" 

⎟ 

⎜ 

λ "I" 

I ⎟ 

⎜ 

⎝ "z" 

λ z 

⎜ 

⎜ 

⎜ 

⎝ 

⎞ 

⎠ 

( ) 

( , , val_Z b , λ ) 

spline_Z b := lspline λ b , val_Z b 

λ := 0.36 ⋅µm, 0.36 ⋅µm 

+ 0.01 ⋅µm.. 

1.25 ⋅µm 

Z b ( λ) := interp spline_Z b λ b 

i := 0.. 

6 

⎞ 

⎟ 

⎟ 

⎟ 

⎠ 

Note that B and z wavelength are set to the limit 

of MUSE wavelength range 

8.5 

Z b 

( λ) 

8 

val_Z bi 

7.5 

7 

0.5 1 

λ 

λ bi 

, 

µm µm



Issue: 1.3 

Date: 28/01/04 

Page: 16/14 

Function to transform magnitude in flux 

Mag2Flux( mag, 

λ) 10 − 0.4⋅mag 

Z b 

:= 

− ( λ) 

⋅W⋅ m − 2 ⋅µm − 1 

SurfMag2Flux( mag, 

λ) 10 − 0.4⋅mag 

Z b 

:= 

− ( λ) 

⋅W⋅ m − 2 ⋅µm − 1 ⋅arcsec − 2 

Flux2Mag( F, 

λ) := −2.5⋅ 

⎡ 

⎢ 

⎣ 

log 

⎡ 

⎢ 

⎣ 

F 

Wm ⋅ − 2 ⋅µm − 1 

( ) 

⎤ 

⎥ 

⎦ 

⎤ 

⎥⎦ 

+ Z b ( λ) 

Flux2MagSurf( F, 

λ) := Flux2Mag( F, 

λ) − 2.5⋅ 

log⎡ ( arcsec 

2 ) 

⎣ 

⎤ 

⎦ 

back 

3.2 AB magnitude system 

Flux2AB F λ , λ 

( ) := −2.5 

⎡ 

F λ ⋅λ 2 

⋅ ⎢ 

⎥ 

log 

⎢ 

c erg⋅cm − 2 ⋅sec − 1 ⋅Hz − 1 − 

⎥ 

48.60 

⎣ 

⋅( ) 

⎤ 

⎦ 

AB2Flux( AB, 

λ) 

− 

10 0.4⋅( 

AB + 48.60 ) 

:= 

λ 2 ⋅c⋅erg⋅cm − 2 ⋅s − 1 ⋅Hz − 1 

Flux2ABSurf F λ , λ 

( ) := −2.5 

⎡ 

F λ ⋅λ 2 

⋅ ⎢ 

⎥ 

log 

⎢ 

c erg⋅cm − 2 ⋅sec − 1 ⋅Hz − 1 ⋅arcsec − 2 − 

⎥ 

48.60 

⎣ 

⋅( ) 

⎤ 

⎦ 

SurfAB2Flux( AB, 

λ) 

− 

10 0.4⋅( 

AB + 48.60 ) 

:= 

λ 2 ⋅c⋅erg⋅cm − 2 ⋅s − 1 ⋅Hz − 1 ⋅arcsec − 2 

Test 

( ( ), 

λ R ) = 25.163 

Flux2AB Mag2Flux 25, 

λ R 

SurfAB2Flux 25, 

λ R 

( ) = 2.657 10 − 19 

× erg⋅cm − 2 ⋅s − 1 ⋅A − 1 ⋅arcsec − 2 

( ) = 2.657 10 − 19 

AB2Flux 25, 

λ R 

× erg⋅cm − 2 ⋅s − 1 ⋅A − 1 

back



Issue: 1.3 

Date: 28/01/04 

Page: 17/14 

4. Sky brightness 

Sky brightness is taken from the Hanuschik paper, it doesnt include the OH lines 

⎛ 

⎜ 

⎜ 

⎜ 

⎜ 

⎜ 

⎜ 

⎜ 

0.44 

0.1 

0.5 ⎟ 

⎜ 0.1 

0.55 ⎟ 

⎜ 0.105 

⎟ 

⎜ 

0.6 

0.095 

⎟ 

⎜ 

0.65 ⎟ 

⎜ 0.08 

⎟ 

⎜ 

0.7 

0.075 

⎜ ⎟ 

⎜ 

λ Sky := ⎜ 0.75 ⎟⋅µm 

TabFlux SkyNoOH := ⎜ 0.075 

⎜ 0.8 ⎟ 

⎜ 0.082 

⎜ ⎟ 

⎜ 0.85 ⎟ 

0.07 

⎜ 0.9 ⎟ 

0.06 

⎜ ⎟ 

⎜ 

0.95 

⎟ 

0.06 

⎜ 1.0 ⎟ 

0.1 

⎜ 

1.025 

0.15 

⎝ 

⎞ 

⎠ 

Spline_Flux SkyNoOH 

⎛ 

⎜ 

⎜ 

⎜ 

⎜ 

⎜ 

⎜ 

⎜ 

⎜ 

⎝ 

:= lspline( λ Sky , TabFlux SkyNoOH ) 

Flux SkyNoOH ( λ) := interp Spline_Flux SkyNoOH , λ Sky , TabFlux SkyNoOH , λ 

⎞ 

⎟ 

⎟ 

⎟ 

⎟ 

⎟ 

⎟ 

⎟ 

⎟⋅ 

⎟ 

⎟ 

⎟ 

⎟ 

⎟ 

⎟ 

⎟ 

( ) 

⎠ 

10 − 16 ⋅erg⋅s − 1 ⋅cm − 2 ⋅A − 1 ⋅arcsec − 2 

λ := 0.44 ⋅µm, 0.47 ⋅µm.. 

1.025 ⋅µm 

Sky Flux in erg/cm²/s/A/arcsec² 

1 .10 17 

2 . 10 17 Wavelength (µm) 

0 

0.4 0.5 0.6 0.7 0.8 0.9 1 1.1



Issue: 1.3 

Date: 28/01/04 

Page: 18/14 

Checking 

( ( ) λ B ) 21.766 

( ( ), 

λ V ) 21.337 

( ( ), 

λ R ) 21.109 

( ( ), 

λ I ) 20.441 

( ( ), 

λ z ) 20.378 

Flux2ABSurf Flux SkyNoOH λ B , 

Flux2ABSurf Flux SkyNoOH λ V 

Flux2MagSurf Flux SkyNoOH λ R 

Flux2MagSurf Flux SkyNoOH λ I 

Flux2MagSurf Flux SkyNoOH λ z 

= ESO ETC value 22.7 





Note the difference in the red is fully explained by the OH suppression. The difference in the 

blue is probably due to the moon light, a moon aged of 5 days would make the difference. 

19 

Sky brightness (excluding OH lines) 

AB magnitude/arcsec² 

20 

21 

22 

0.4 0.6 0.8 1 

Wavelength (µm) 

back



Issue: 1.3 

Date: 28/01/04 

Page: 19/14 

tab_extinct:= 

0 

1 

2 

3 

4 

5 

6 

7 

8 

9 

0 1 

440 0.26 

450 0.25 

460 0.22 

470 0.21 

480 0.21 

490 0.18 

500 0.17 

520 0.16 

540 0.14 

560 0.13 

5. Atmospheric extinction 

Reference: Paranal extinction - ESO 

VIMOS ETC 

( ) 0 

λext 10 − 3 

〈〉 

⋅ µm 

〈 

:= ⋅ tab_extinct val_extinct tab_extinct 1 〉 

:= 

⎛ 

⎝ 

pol_extinct:= 

régress⎜ 

λext 

µm , val_extinct, 

4 

⎛ 

⎝ 

λext 

λ 

int_extinct ( λ) := interp⎜ 

pol_extinct, , val_extinct, 

µm 

µm 

⎞ 

⎠ 

⎞ 

⎠ 

Extinct( λ , Airmass) := 10 − 0.4⋅int_extinctλ 

( ) ⋅Airmass 

λ := 0.45 ⋅µm, 0.46 ⋅µm.. 

1 ⋅µm 

0.4 

int_extinctλ ( ) 

val_extinct 

0.2 

0 

0.5 0.6 0.7 0.8 0.9 

λ ⋅µm − 1 , λext ⋅µm − 1 

1 

Extinction Factor 

Extinct( λ, 

1.0) 

0.9 

0.8 

0.4 0.5 0.6 0.7 0.8 0.9 1 

λ ⋅µm − 1 

back



Issue: 1.3 

Date: 28/01/04 

Page: 20/14 

6. Telescope Effective Area 

Area VLT := 485425.1cm ⋅ 

2 From ESO UVES ETC 

Note: Useful surface is only 

Area VLT 

π ⋅( 4⋅m) 2 

= 0.966 

back



Issue: 1.3 

Date: 28/01/04 

Page: 21/14 

7. MUSE throughput 

7.1 MUSE throughput of WF mode 

table_muse_throughput:= 

0 

1 

2 

3 

0 1 

0.46 0.0784 

0.48 0.1217 

0.5 0.1681 

0.52 0.2092 

Total throughput of MUSE, 

excluding atmosphere, version 

20/11/03 

Typical curve with Adaptive 

Secondary 

4 

0.54 0.2452 

5 

0.56 0.273 

6 

0.58 0.2923 

7 

0.585 0.2973 

8 

0.59 0.3012 

9 

0.595 0.3046 

〈〉 

λ_table := µm⋅ 

table_muse_throughput 0 

〈 

val_muse_throughput table_muse_throughput 1 〉 

:= 

T MUSE ( λ) := interplin( λ_table, val_muse_throughput, 

λ) 

( ) = 0.239 

λ min := 0.465 ⋅µm 

λ max := 0.93 ⋅µm 

Mean T MUSE , λ min , λ max 

λ := λ min , λ min + 0.001 ⋅µm.. 

λ max 

0.4 

MUSE+VLT Total Throughput 

0.35 

0.3 

0.25 

T MUSE 

( λ) 

0.2 

0.15 

0.1 

0.05 

0 

0.4 0.5 0.6 0.7 0.8 0.9 1 

λ 

µm 

back



Issue: 1.3 

Date: 28/01/04 

Page: 22/14 

7.2 MUSE throughput of HR mode



Issue: 1.3 

Date: 28/01/04 

Page: 23/14 

DN CCD 

RN CCD 

:= Dark Current 

3⋅ 

elec 

hour 

8. MUSE CCD characteristics 

:= 4⋅elec 

Readout noise Note; This is for Fairchild CCD 

Npix CCD := 4096 

back



Issue: 1.3 

Date: 28/01/04 

Page: 24/14 

9. MUSE spatial and spectral configurations 

9.1 MUSE wide-field spatial mode 

∆ WFspa := 

0.2⋅ 

arcsec 

9.2 MUSE high spatial resolution mode 

∆ HRspa 

:= 0.025⋅ 

arcsec 

λ min := 0.465 ⋅µm 

λ max := 0.93 ⋅µm 

( ) 

9.3 MUSE Spectral characteristics 

λ max − λ min 

∆ spec := ∆ 

Npix spec = 1.135A λ := λ min , λ min + ∆ spec .. λ max 

CCD 

back



Issue: 1.3 

Date: 28/01/04 

Page: 25/14 

10. MUSE Spatial PSF 

10.1 MUSE spatial PSF in WF mode 

10.1.1 Seeing limited, poor seeing conditions 

TabEEnoao poor := 

0 1 2 3 4 5 6 

0 

1 

2 

3 

4 

5 

6 

7 

8 

9 

0.2 0.0303 0.0332 0.0344 0.0394 0.0421 0.04 

0.4 0.1087 0.1174 0.1257 0.1345 0.1415 0.1525 

0.6 0.2251 0.2412 0.2529 0.2724 0.2848 0.2926 

0.8 0.3566 0.3725 0.3915 0.4109 0.4254 0.4377 

1 0.4853 0.504 0.5281 0.546 0.5613 0.5764 

1.2 0.5954 0.6133 0.6376 0.6514 0.6713 0.6795 

1.4 0.6903 0.7062 0.7244 0.7385 0.7496 0.7576 

1.6 0.7653 0.7778 0.7904 0.7998 0.8115 0.8187 

1.8 0.8208 0.8295 0.8394 0.8475 0.8535 0.8593 

2 0.8644 0.8698 0.8755 0.882 0.8861 0.8907 

i := 0.. 

18 

j := 1.. 

6 

Note that MUSE IQE is now included in ensquared energy 

( ) T 

EE := sousmatrice TabEEnoao i 

poor , i , i , 1, 

6 

⎛ 

⎜ 

⎜ 

⎜ 

⎜ 

⎜ 

⎝ 

⎞ 

0.465 

0.55 ⎟ 

⎜ 0.65 ⎟ 

λ EE := ⎜ ⎟⋅µm 

0.75 

⎟ 

0.85 ⎟ 

0.93 

D EEi := TabEEnoao poori , 0 

⎠ 

k := 0.. 

5 

T := k 

cspline( λ EE , EE k ) 

( ) 

EEnoao poor ( λ , k) := interp T , λ k−1 

EE , EE , λ k−1 

back



Issue: 1.3 

Date: 28/01/04 

Page: 26/14 

10.1.2 Seeing limited, good seeing conditions 

TabEEnoao good := 

0 1 2 3 4 5 6 

0 

1 

2 

3 

4 

5 

6 

7 

8 

9 

0.2 0.067 0.0732 0.0757 0.0859 0.0913 0.0868 

0.4 0.2249 0.2409 0.2557 0.2709 0.2824 0.3006 

0.6 0.422 0.4458 0.4617 0.4879 0.5033 0.5125 

0.8 0.598 0.6155 0.6351 0.6541 0.6672 0.678 

1 0.7295 0.7445 0.7627 0.7749 0.7848 0.7944 

1.2 0.8157 0.8261 0.8397 0.8464 0.8564 0.8598 

1.4 0.8739 0.8805 0.888 0.8933 0.8972 0.8999 

1.6 0.9112 0.9149 0.9185 0.921 0.9247 0.9267 

1.8 0.9348 0.9363 0.9385 0.9403 0.9415 0.943 

2 0.9516 0.9516 0.9521 0.9533 0.9539 0.9549 

i := 0.. 

18 

j := 1.. 

6 


( ) T 

EE := sousmatrice TabEEnoao i 

good , i , i , 1, 

6 

⎛ 

⎜ 

⎜ 

⎜ 

⎜ 

⎜ 

⎝ 

⎞ 

0.465 

0.55 ⎟ 

⎜ 0.65 ⎟ 

λ EE := ⎜ ⎟⋅µm 

0.75 

⎟ 

0.85 ⎟ 

0.93 

D EEi := TabEEnoao goodi , 0 

⎠ 

k := 0.. 

5 

T := k 


( ) 

EEnoao good ( λ , k) := interp T , λ k−1 

EE , EE , λ k−1 

back



Issue: 1.3 

Date: 28/01/04 

Page: 27/14 

10.1.3 AO Gen I, poor seeing conditions 

TabEEgenI poor := 

0 1 2 3 4 5 6 7 

0 

1 

2 

3 

4 

5 

6 

7 

8 

9 

1 0.1 0.0554 0.0653 0.0734 0.0894 0.1009 0.1003 

2 0.2 0.1839 0.2091 0.2365 0.2647 0.2893 0.3169 

3 0.3 0.343 0.3777 0.4089 0.4478 0.4769 0.4968 

4 0.4 0.4897 0.5182 0.5502 0.5804 0.6046 0.6233 

5 0.5 0.6089 0.633 0.6607 0.6818 0.6998 0.715 

6 0.6 0.6973 0.7152 0.7369 0.7504 0.7667 0.7746 

7 0.7 0.7665 0.7789 0.7929 0.8037 0.8123 0.8184 

8 0.8 0.8182 0.826 0.8343 0.8408 0.8486 0.8535 

9 0.9 0.8558 0.8598 0.8655 0.8705 0.8744 0.8782 

10 1 0.8857 0.8869 0.8894 0.8932 0.8957 0.8988 

i := 0.. 

18 

j := 1.. 

6 

EE := sousmatrice TabEEgenI i 

poor , i , i , 1, 

6 

⎛ 

⎜ 

⎜ 

⎜ 

⎜ 

⎜ 

⎝ 

⎞ 

0.465 

0.55 ⎟ 

⎜ 0.65 ⎟ 

λ EE := ⎜ ⎟⋅µm 

0.75 

⎟ 

0.85 ⎟ 

0.93 

D EEi := TabEEgenI poori , 0 

⎠ 


( ) T 

k := 0.. 

5 

T := k 


( ) 

EEgenI poor ( λ , k) := interp T , λ k−1 

EE , EE , λ k−1 

back



Issue: 1.3 

Date: 28/01/04 

Page: 28/14 

10.1.4 AO Gen I, good seeing conditions 

TabEEgenI good := 

0 1 2 3 4 5 6 

0 

1 

2 

3 

4 

5 

6 

7 

8 

9 

0.2 0.1151 0.1325 0.1442 0.1688 0.1839 0.1789 

0.4 0.3384 0.3732 0.4072 0.4393 0.4647 0.4939 

0.6 0.5479 0.5828 0.6109 0.6447 0.6678 0.6828 

0.8 0.6927 0.7134 0.7353 0.7551 0.7703 0.7819 

1 0.7861 0.7996 0.8147 0.8256 0.8346 0.8426 

1.2 0.8448 0.8529 0.8631 0.8686 0.8762 0.8794 

1.4 0.8857 0.8904 0.8959 0.8999 0.9031 0.9054 

1.6 0.9139 0.9163 0.9189 0.9208 0.9238 0.9255 

1.8 0.9333 0.9341 0.9356 0.937 0.938 0.9393 

2 0.9483 0.9479 0.9481 0.9491 0.9495 0.9505 

i := 0.. 

18 

j := 1.. 

6 

EE := sousmatrice TabEEgenI i 

good , i , i , 1, 

6 

⎛ 

⎜ 

⎜ 

⎜ 

⎜ 

⎜ 

⎝ 

⎞ 

0.465 

0.55 ⎟ 

⎜ 0.65 ⎟ 

λ EE := ⎜ ⎟⋅µm 

0.75 

⎟ 

0.85 ⎟ 

0.93 

D EEi := TabEEgenI goodi , 0 

⎠ 


( ) T 

k := 0.. 

5 

T := k 


( ) 

EEgenI good ( λ , k) := interp T , λ k−1 

EE , EE , λ k−1 

back



Issue: 1.3 

Date: 28/01/04 

Page: 29/14 

10.2 MUSE spatial PSF in HR mode 

10.2.1 AO Gen II, good seeing conditions 

TabEEgenII good := 

0 1 2 3 4 5 6 

0 

1 

2 

3 

4 

5 

6 

7 

8 

9 

0.025 0.0735 0.1362 0.2195 0.3019 0.1954 0.2321 

0.05 0.1848 0.2586 0.3131 0.4066 0.4835 0.4297 

0.075 0.2183 0.3111 0.4008 0.4611 0.5327 0.5782 

0.1 0.2468 0.3357 0.4319 0.505 0.557 0.6007 

0.125 0.2628 0.3537 0.4422 0.5169 0.5721 0.6152 

0.15 0.2787 0.3699 0.4597 0.5356 0.5931 0.6263 

0.175 0.3032 0.3858 0.4753 0.5436 0.6014 0.6355 

0.2 0.3201 0.4018 0.4902 0.5581 0.6089 0.6508 

0.225 0.3375 0.4182 0.4976 0.565 0.6224 0.6574 

0.25 0.3642 0.4349 0.5126 0.5786 0.6288 0.6636 

i := 0.. 

18 

j := 1.. 

6 

EE := sousmatrice TabEEgenII i 

good , i , i , 1, 

6 

⎛ 

⎜ 

⎜ 

⎜ 

⎜ 

⎜ 

⎝ 

⎞ 

0.465 

0.55 ⎟ 

⎜ 0.65 ⎟ 

λ EE := ⎜ ⎟⋅µm 

0.75 

⎟ 

0.85 ⎟ 

0.93 

D EEi := TabEEgenII goodi , 0 

k := 0.. 

5 

T := k 

⎠ 



( ) T 

( ) 

EEgenII good ( λ , k) := interp T , λ k−1 

EE , EE , λ k−1



Issue: 1.3 

Date: 28/01/04 

Page: 30/14 

10.3 Number of spatial pixels 

In the case of unresolved objects and in good seeing conditions we will sum up 3x3 spatial pixels 

to recover a fraction of the object flux, this correspond to 0.6x0.6 arcsec² in WF mode and 

0.075x0.075 arcsec² in HR mode 

k spa_good := 3 

EEnoao good ( λ V , k spa_good ) 0.446 

EEgenI good ( λ V , k spa_good ) 0.583 

EEgenII good ( λ V , k spa_good ) 0.311 

( ) = 0.496 

= EEnoao good λ I , k spa_good 

( ) = 0.655 

= EEgenI good λ I , k spa_good 

( ) = 0.489 

= EEgenII good λ I , k spa_good 

In the case of unresolved objects and in poor seeing conditions we will sum up 4x4 spatial pixels 

to recover a fraction of the object flux, this correspond to 0.8x0.8 arcsec² in WF mode and 

0.1x0.1 arcsec² in HR mode 

k spa_poor := 4 

EEnoao poor ( λ V , k spa_poor ) 0.373 

EEgenI poor ( λ V , k spa_poor ) 0.49 

( ) = 0.417 

= EEnoao poor λ I , k spa_poor 

( ) = 0.563 

= EEgenI poor λ I , k spa_poor 

Note that this choice is somewhat arbitrary. It is a trade between S/N and spatial resolution. Optimum 

summation should allow increase of the S/N while keeping the spatial resolution.



Issue: 1.3 

Date: 28/01/04 

Page: 31/14 

11. MUSE spectral PSF 

11.1 Shape of spectral PSF 

The spectral PSF is assumed to be Gaussian with 2*pixels FWHM 

back 

λ 

FWHM spec := 2⋅∆ spec 

R spec ( λ) 

:= 

FWHM spec 

( ) 

( ) 

R min := R spec λ min 

R max := R spec λ max 

R min = 2.048× 

10 3 

R max = 4.096× 

10 3 

R spec 

( λ) 

4500 

3500 

2500 

1500 

0.4 0.5 0.6 0.7 0.8 0.9 1 

λ 

µm 

R min + R max 

R mean := R 

2 

mean = 3.072× 

10 3 

Fraction of energy enclosed within n pixels : 

( ) 

FracE spec ( n) := E GAUSS n, 

σ GAUSS ( 2) 

i := 2.. 

4 FracE spec () i 

0.761 

0.923 

0.981 

=



Issue: 1.3 

Date: 28/01/04 

Page: 32/14 

Low spectral resolution is obtained after summation of N spectral pixels 

N sumspec := 10 

∆ lowspec := N sumspec ⋅∆ spec ∆ lowspec = 11.353A 

R lowspec ( λ) 

:= 

λ 

2⋅∆ lowspec 

R lowspec ( λ min ) 

R lowmin := R lowmin = 204.8 

R lowspec ( λ max ) 

R lowmax := R lowmax = 409.6 

R lowmin + R lowmax 

R lowmean := R 

2 

lowmean = 307.2 

back 

11.2 Number of spectral pixels 

To recover major party of the fllux of an emission line we sum over 3 pixels in the spectral direction 

k spec := 3 

FracE spec ( k spec ) = 0.923



Issue: 1.3 

Date: 28/01/04 

Page: 33/14 

Signal to Noise SN 

Main ETC Formula 

( ) := n⋅K O 

SN F O , n, t, F S , RN , DC , K O , K S , K RN , K DC 

⋅F O ⋅ t⋅ 

⎛ 

⎜ 

⎜ 

⎝ 

K O ⋅F O ⋅ t + K S ⋅F S ⋅ t ... 

+ K RN ⋅RN 2 + K DC ⋅DC⋅ 

t 

⎞ 

⎠ 

− 1 

2 

Object Flux F O 

( ) := a ← n 

F O SN, n, t, F S , RN , DC , K O , K S , K RN , K DC 

b ← 

⎛ 

⎜ 

⎝ 

K O ⋅ t 

K O ⋅ t 

SN 

⎞ 

⎠ 

2 

c ← K S ⋅F S ⋅ t + K DC ⋅DC⋅ 

t + K RN ⋅RN 2 

Integration time t 

( ) := a ← n 

tSNn , , F O , F S , RN , DC , K O , K S , K RN , K DC 

b + b 2 + 4⋅a⋅c 

2⋅a 

b ← 

⎛ 

⎜ 

⎝ 

K O ⋅F O 

SN 

K O ⋅F O 

c ← K RN ⋅RN 2 

⎞ 

⎠ 

2 

+ K S ⋅F S + K DC ⋅DC 

b + b 2 + 4⋅a⋅c 

2⋅a 

Where F O 

is the Object Flux ( erg s − 1 

⋅ ⋅ cm − 2 ) 

if it is a surface brightness, flux should be in 

arcsec − 2 

if it is a continuum source, flux should be in A − 1 

and F S 

is the Sky Flux ( erg⋅s − 1 ⋅cm − 2 ⋅A − 1 ⋅ arcsec − 2 ) 

and n is the number of exposures 

and t is the integration time in sec of one exposure 

and RN is the readout noise in electron per pixel 

and DC is the dark current in electron per pixel and per 

hour 

The coefficient K O 

transform the object flux in photons per second 

the coefficients K O , K S , K RN , K DC 

are defined below 

( ) f s ⋅∆ s 

K O f s , f a , ∆ s , ∆ a , λ, 

A m := 

2 

⋅f a ⋅∆ a ⋅T MUSE ( λ) 

⋅Area VLT ⋅ 

( ) 

Extinct λ , A m 

λ 

⋅ 

hc ⋅



Issue: 1.3 

Date: 28/01/04 

Page: 34/14 

Where f s 

is the fraction of total flux enclosed in a spectral bin 

and f a 

is the fraction of total flux enclosed in a spatial bin 

and ∆ s 

is the size of a spectral bin 

and ∆ a 

is the linear size of a spatial bin in arcsec 

and λ is the wavelength 

and A m 

is the airmass 

and T MUSE 

is the MUSE+VLT total throughput 

and Area VLT 

is the effective collective area of VLT primary mirror 

and Extinc is the extinction absorption coefficient at Paranal 

Note that when the flux is a flat continuum source (flux per A) 

f s 

must be set to 1 and ∆ s 

to the size of the spectral bin 

and when the flux is an emission source (flux not per A) 

f s 

must be set to the flux fraction enclosed in the bin and ∆ s 

to 1 

Note that when the flux is a surface brightness source (flux per arcsec²) 

f a 

must be set to 1 and ∆ a 

to the size of the spectral bin 

and when the flux is a total flux (flux not per arcsec²) 

f a 

must be set to the flux fraction enclosed in the bin and ∆ a 

to 1 

The coefficient K S 

transform the sky flux in photons per second 

2 

( ) := ∆ s ⋅∆ a 

K S ∆ s , ∆ a , λ 

λ 

⋅T MUSE ( λ) 

⋅Area VLT ⋅ 

hc ⋅ 

The coefficient K RN 

is the number of summed bin 

The coefficient K DC 

is the number of summed pixels 

back



Issue: 1.3 

Date: 28/01/04 

Page: 35/14 

Noise Statistics 

( ) := V O ← K O ⋅F O 

FNoise F O , n, t, F S , RN, DC, K O , K S , K RN , K DC 

⋅n⋅ 

t 

V S ← K S ⋅F S ⋅n⋅ 

t 

V RN ← nK ⋅ RN ⋅RN 2 

V DC ← nK ⋅ DC ⋅DC⋅ 

t 

V CCD ← V RN + V DC 

V Tot ← V O + V S + V CCD 

⎛ 

⎜ 

⎜ 

⎜ 

⎜ 

⎜ 

⎜ 

⎜ 

⎜ 

⎜ 

⎜ 

⎜ 

⎜ 

⎜ 

⎜ 

⎜ 

⎝ 

V O 

V Tot 

V S 

V Tot 

V RN 

V Tot 

V DC 

V Tot 

V CCD 

V Tot 

⎞ 

⎟ 

⎟ 

⎟ 

⎟ 

⎟ 

⎟ 

⎟ 

⎟ 

⎟ 

⎟ 

⎟ 

⎟ 

⎟ 

⎠ 

This function give the fraction of noise due to object (line 1), sky (line 2), readout (line 3), dark current 

(line 4), detector (ie readout + drak current, line 5) 

back



Issue: 1.3 

Date: 28/01/04 

Page: 36/14 

13. ETC parameters 

SN lim := 5 Signal to Noise 

t exp 

:= 1⋅hour 

Exposure time 

n exp := 80 Number of summed exposures 

AM := 1 Air mass of observations 

F Sky ( λ) Flux SkyNoOH ( λ) 

:= Sky flux is taken outside OH lines 

back



Issue: 1.3 

Date: 28/01/04 

Page: 37/14 

14. Limiting surface brightness 

Estimation of limiting surface brightness for a continuum source with flat spectra. The 

computation is done by spectral and spatial pixels. 

14.1 WF mode 

back 

( ) 

( , , λ) 

K Obj ( λ) := K O 11 , , ∆ spec , ∆ WFspa , λ, 

AM 

K Sky ( λ) := K S ∆ spec ∆ WFspa 

K RN := 1 

K DC := 1 

( ) 

LimSurfFWF( λ) := F O SN lim , n exp , t exp , F Sky ( λ) 

, RN CCD , DN CCD , K Obj ( λ) 

, K Sky ( λ) 

, K RN , K DC 

2 .10 18 

LimSurfFWF( λ) 

1 .10 18 

erg⋅s − 1 ⋅cm − 2 ⋅A − 1 ⋅arcsec − 2 

0.4 0.5 0.6 0.7 0.8 0.9 1 

λ 

µm 

i := 0.. 

4 

back 

⎛ 

LimMagSurfWF := Flux2ABSurf LimSurfFWF λ i 

MUSEi , 

⎝ ⎠ λ MUSE 

⎝ 

i 

⎛ 

⎞ 

⎞ 

⎠ 

⎛ 

23.228 

"B" 

⎜ 

⎜ 

23.87 

"V" 

⎜ ⎟ 

⎜ 

LimMagSurfWF = ⎜ 23.928⎟ 

Band MUSE = ⎜ "R" 

⎜ 23.479⎟ 

"I" 

⎜ 

22.778 

"z" 

⎝ 

⎞ 

⎠ 

⎛ 

⎜ 

⎜ 

⎝ 

⎞ 

⎟ 

⎟ 

⎟ 

⎠ 

back



Issue: 1.3 

Date: 28/01/04 

Page: 38/14 

( ) 

FN( λ) := FNoise LimSurfFWF( λ) , n exp , t exp , F Sky ( λ) 


, K Sky ( λ) 

, K RN , K DC 

FN( λ V ) 

⎛ 

0.061 

⎜ 

0.712 

⎜ ⎟ 

= ⎜ 0.191⎟ 

FN λ R 

⎜ 0.036⎟ 

⎜ 

0.226 

⎝ 

⎞ 

⎠ 

( ) 

Computing line emission sensitivity by arcsec 

We sum the emission line over 3 pixels 

k spec := 3 

FracE spec ( k spec ) = 0.923 

( ( ) 1 

) 

K Obj ( λ) := K O FracE spec k spec , , 1, ∆ WFspa , λ, 

AM 

( ) 

K Sky ( λ) := K S k spec ⋅∆ spec , ∆ WFspa , λ 

K RN 

K DC 

:= k spec 

:= K RN 

⎛ 

0.058 

⎜ 

0.734 

⎜ ⎟ 

= ⎜ 0.175⎟ 

FN λ z 

⎜ 0.033⎟ 

⎜ 

0.208 

⎝ 

⎞ 

⎠ 

( ) 

= 

⎛ 

⎜ 

⎜ 

⎜ 

⎜ 

⎜ 

⎝ 

0.083 

0.499 

0.352 

0.066 

0.418 

⎞ 

⎟ 

⎟ 

⎟ 

⎠ 

( ) 

LimFLineSurfWF( λ) := F O SN lim , n exp , t exp , F Sky ( λ) 


, K Sky ( λ) 

, K RN , K DC 

LimFLineSurfWF λ B 

( ) = 5.38 10 − 18 

× erg⋅s − 1 ⋅cm − 2 ⋅arcsec − 2 

5 . 10 18 

LimFLineSurfWFλ ( ) 

3.37 .10 18 

erg⋅s − 1 ⋅cm − 2 ⋅arcsec − 2 

1.73 . 10 18 

1 .10 19 

0.4 0.5 0.6 0.7 0.8 0.9 1 

λ 

µm



Issue: 1.3 

Date: 28/01/04 

Page: 39/14 

LimVFLineSurfWF i 

:= LimFLineSurfWF⎛ 

λ 

⎝ MUSEi ⎞ 

⎠ 

i := 0.. 

4 

LimVFLineSurfWF = 

⎛ 

⎜ 

⎜ 

⎜ 

⎜ 

⎜ 

⎜ 

⎜ 

⎝ 

5.38× 

10 − 18 

2.143× 

10 − 18 

1.501× 

10 − 18 

1.489× 

10 − 18 

2.038× 

10 − 18 

⎞ 

⎟ 

⎟ 

⎟ 

⎟ 

⎟ 

⎠ 

erg⋅s − 1 ⋅cm − 2 ⋅arcsec − 2 

Band MUSE = 

⎛ 

⎜ 

⎜ 

⎜ 

⎜ 

⎜ 

⎝ 

"B" 

"V" 

"R" 

"I" 

"z" 

⎞ 

⎟ 

⎟ 

⎟ 

⎠



Issue: 1.3 

Date: 28/01/04 

Page: 40/14 

14.2 HR mode 

back 

Nota that the computation is done for a single 1 hour integration 

( ) 

( , , λ) 

K Obj ( λ) := K O 11 , , ∆ spec , ∆ HRspa , λ, 

AM 

K Sky ( λ) := K S ∆ spec ∆ HRspa 

K RN := 1 

K DC := 1 

F Sky ( λ) := Flux SkyNoOH ( λ) 

( ) 

LimSurfFHR( λ) := F O SN lim , 1, t exp , F Sky ( λ) 


, K Sky ( λ) 

, K RN , K DC 

1.5 .10 15 

1 .10 15 

LimSurfFHR( λ) 

erg⋅s − 1 ⋅cm − 2 ⋅A − 1 ⋅arcsec − 2 

5 .10 16 

0.4 0.5 0.6 0.7 0.8 0.9 1 

λ 

µm 

LimMagSurfHR i 

i := 0.. 

4 

:= Flux2ABSurf⎛ 

LimSurfFHR⎛ 

λ 

⎝ MUSEi ⎞ , 

⎠ λ MUSE ⎞ 

back 

⎝ 

i ⎠ 

⎛ 

16.126 

"B" 

⎜ 

⎜ 

17.175 

"V" 

⎜ ⎟ 

⎜ 

LimMagSurfHR = ⎜ 17.278⎟ 


⎜ 16.803⎟ 

"I" 

15.76 

"z" 

⎜ 

⎝ 

⎞ 

⎠ 

⎛ 

⎜ 

⎜ 

⎝ 

⎞ 

⎟ 

⎟ 

⎟ 

⎠



Issue: 1.3 

Date: 28/01/04 

Page: 41/14 

( ) 

FN( λ) := FNoise LimSurfFHR( λ) , 1, t exp , F Sky ( λ) 


, K Sky ( λ) 

, K RN , K DC 

FN( λ V ) 

⎛ 

0.656 

⎜ 

0.016 

⎜ ⎟ 

= ⎜ 0.276⎟ 

FN λ R 

⎜ 0.052⎟ 

⎜ 

0.327 

⎝ 

⎞ 

⎠ 

( ) 

⎛ 

0.655 

⎜ 

0.018 

⎜ ⎟ 

= ⎜ 0.275⎟ 

FN λ z 

⎜ 0.052⎟ 

⎜ 

0.327 

⎝ 

⎞ 

⎠ 

( ) 

= 

⎛ 

⎜ 

⎜ 

⎜ 

⎜ 

⎜ 

⎜ 

⎝ 

0.661 

6.201× 

10 − 3 

0.28 

0.052 

0.332 

⎞ 

⎟ 

⎟ 

⎟ 

⎟ 

⎠ 

back



Issue: 1.3 

Date: 28/01/04 

Page: 42/14 

15. Limiting flux for an unresolved source 

15.1 WF mode 

15.1.1 Seeing limited, poor seeing conditions 

i := 0.. 

4 

( ) 

EE spa ( λ) := EEnoao poor λ , k spa_poor 

EE spa 

( λ) 

0.4 

0.35 

0.4 0.5 0.6 0.7 0.8 0.9 1 

λ 

µm 

back 

K Obj ( λ) := K O 1, EE spa ( λ) 

, ∆ spec , 1 , λ, 

AM 

15.1.1.1 Continuum source 

( ) 

( ) 

K Sky ( λ) := K S ∆ spec , k spa_poor ⋅∆ WFspa , λ 

2 

K RN := k spa_poor 

2 

K DC := k spa_poor 

( ) 

LimFContWFnoao poor ( λ) := F O SN lim , n exp , t exp , F Sky ( λ) 


, K Sky ( λ) 

, K RN , K DC 

1 . 10 18 

LimFContWFnoao poor 

( λ) 

erg⋅s − 1 ⋅cm − 2 ⋅A − 1 

5 .10 19 

0.4 0.5 0.6 0.7 0.8 0.9 1 

λ 

µm



Issue: 1.3 

Date: 28/01/04 

Page: 43/14 

⎛ 

LimMagContWFnoao poori := Flux2AB LimFContWFnoao poor λ MUSEi , 


⎝ 

i 

i := 0.. 

4 

⎛ 

24.136 

"B" 

⎜ 

⎜ 

24.813 

"V" 

⎜ ⎟ 

⎜ 

LimMagContWFnoao poor = ⎜ 24.918⎟ 


⎜ 24.544⎟ 

"I" 

⎜ 

23.906 

"z" 

In case of lower dispersion we have 


, ∆ lowspec , 1 , λ, 

AM 

⎝ 

( ) 

( ) 

K Sky ( λ) := K S ∆ lowspec , k spa_poor ⋅∆ WFspa , λ 

2 

K RN := N sumspec k spa_poor 

⎞ 

⎠ 

⎛ 

⎛ 

⎜ 

⎜ 

⎝ 

⎞ 

⎞ 

⎟ 

⎟ 

⎟ 

⎠ 

⎞ 

⎠ 

K DC 

:= K RN 

( ) 

LimFContWFnoao poor ( λ) := F O SN lim , n exp , t exp , F Sky ( λ) 


, K Sky ( λ) 

, K RN , K DC 

⎛ 

LimMagContLowWFnoao poori := Flux2AB LimFContWFnoao poor λ 

⎝ MUSEi , 

⎠ λ MUSE 

⎝ 

i 

i := 0.. 

4 

⎛ 

⎞ 

⎞ 

⎠ 

⎛ 

25.395 

"B" 

⎜ 

⎜ 

26.069 

"V" 

⎜ ⎟ 

⎜ 

LimMagContLowWFnoao poor = ⎜ 26.174⎟ 


⎜ 25.8 ⎟ 

"I" 

⎜ 

25.164 

"z" 

⎝ 

⎞ 

⎠ 

⎛ 

⎜ 

⎜ 

⎝ 

⎞ 

⎟ 

⎟ 

⎟ 

⎠ 

back



Issue: 1.3 

Date: 28/01/04 

Page: 44/14 

We sum the emission line over 3 pixels 

k spec := 3 

FracE spec ( k spec ) = 0.923 

15.1.1.2 Line emission source 

( ( ) EE spa λ ) 

K Obj ( λ) := K O FracE spec k spec , ( ), 1, 1 , λ, 

AM 

( ) 

K Sky ( λ) := K S k spec ⋅∆ spec , k spa_poor ⋅∆ WFspa , λ 

K RN 

K DC 

2 

:= k spa_poor ⋅ kspec 

:= K RN 

( ) 

LimFLineWFnoao poor ( λ) := F O SN lim , n exp , t exp , F Sky ( λ) 


, K Sky ( λ) 

, K RN , K DC 

2 . 10 18 

1.37 . 10 18 

LimFLineWFnoao poor 

( λ) 

erg⋅s − 1 ⋅cm − 2 

7.33 . 10 19 

1 .10 19 

0.4 0.5 0.6 0.7 0.8 0.9 1 

λ 

µm 

LimVFLineWFnoao poori 

:= LimFLineWFnoao poor ⎛ λ 

⎝ MUSEi ⎞ 

⎠ 

i := 0.. 

4 

⎛ 

⎜ 

2.365× 

10 − 18 

⎜ 9.082× 

10 − 19 

⎜ 

LimVFLineWFnoao poor = ⎜ 6.087× 

10 − 19 

⎜ 

⎜ 

⎜ 

⎝ 

5.636× 

10 − 19 

7.312× 

10 − 19 

⎞ 

⎟ 

⎟ 

⎟ 

⎟ 

⎟ 

⎠ 

erg⋅s − 1 ⋅cm − 2 

Band MUSE = 

⎛ 

⎜ 

⎜ 

⎜ 

⎜ 

⎜ 

⎝ 

"B" 

"V" 

"R" 

"I" 

"z" 

⎞ 

⎟ 

⎟ 

⎟ 

⎠ 

back



Issue: 1.3 

Date: 28/01/04 

Page: 45/14 

( ) 

FN( λ) := FNoise LimFLineWFnoao poor ( λ) , n exp , t exp , F Sky ( λ) 


, K Sky ( λ) 

, K RN , K DC 

FN( λ V ) 

⎛ 

⎜ 

⎜ 

⎜ 

⎜ 

⎜ 

⎜ 

⎝ 

9.05× 

10 − 3 

0.752 

⎞ 

⎟ 

⎟ 

= 0.201 

FN λ 

⎟ 

R 

0.038 ⎟ 

0.239 

⎠ 

( ) 

⎛ 

⎜ 

⎜ 

⎜ 

⎜ 

⎜ 

⎜ 

⎝ 

8.661× 

10 − 3 

0.772 

= 0.184 

FN λ 

⎟ 

z 

0.035 ⎟ 

0.219 

⎞ 

⎟ 

⎟ 

⎠ 

( ) 

= 

⎛ 

⎜ 

⎜ 

⎜ 

⎜ 

⎜ 

⎝ 

0.012 

0.537 

0.379 

0.071 

0.45 

⎞ 

⎟ 

⎟ 

⎟ 

⎠ 

back



Issue: 1.3 

Date: 28/01/04 

Page: 46/14 

15.1.2 Seeing limited, good seeing conditions 

back 

i := 0.. 

4 

( ) 

EE spa ( λ) := EEnoao good λ , k spa_good 

0.55 

EE spa 

( λ) 

0.5 

0.45 

0.4 

0.4 0.5 0.6 0.7 0.8 0.9 1 

λ 

µm 


back 

( ) 


, ∆ spec , 1 , λ, 

AM 

( ) 

K Sky ( λ) := K S ∆ spec , k spa_good ⋅∆ WFspa , λ 

2 

K RN := k spa_good 

2 

K DC := k spa_good 

( ) 

LimFContWFnoao good ( λ) := F O SN lim , n exp , t exp , F Sky ( λ) 


, K Sky ( λ) 

, K RN , K DC 

5 .10 19 

LimFContWFnoao good 

( λ) 

erg⋅s − 1 ⋅cm − 2 ⋅A − 1 

0.4 0.5 0.6 0.7 0.8 0.9 1 

λ 

µm 

⎛ 

LimMagContWFnoao goodi := Flux2AB LimFContWFnoao good λ 

⎝ MUSEi , 

⎠ λ MUSE 

⎝ 

i 

⎛ 

⎞ 

⎞ 

⎠



Issue: 1.3 

Date: 28/01/04 

Page: 47/14 

i := 0.. 

4 

⎛ 

24.627 

"B" 

⎜ 

⎜ 

25.317 

"V" 

⎜ ⎟ 

⎜ 

LimMagContWFnoao good = ⎜ 25.408⎟ 


⎜ 25.041⎟ 

"I" 

⎜ 

24.386 

"z" 



, ∆ lowspec , 1 , λ, 

AM 

⎝ 

( ) 

( ) 

K Sky ( λ) := K S ∆ lowspec , k spa_good ⋅∆ WFspa , λ 

2 

K RN := N sumspec k spa_good 

⎞ 

⎠ 

⎛ 

⎜ 

⎜ 

⎝ 

⎞ 

⎟ 

⎟ 

⎟ 

⎠ 

K DC 

:= K RN 

( ) 

LimFContWFnoao good ( λ) := F O SN lim , n exp , t exp , F Sky ( λ) 


, K Sky ( λ) 

, K RN , K DC 

⎛ 

LimMagContLowWFnoao goodi := Flux2AB LimFContWFnoao good λ MUSEi , 


⎝ 

i 

i := 0.. 

4 

⎛ 

⎞ 

⎞ 

⎠ 

⎛ 

25.889 

"B" 

⎜ 

⎜ 

26.575 

"V" 

⎜ ⎟ 

⎜ 

LimMagContLowWFnoao good = ⎜ 26.665⎟ 


⎜ 26.299⎟ 

"I" 

⎜ 

25.647 

"z" 

⎝ 

⎞ 

⎠ 

⎛ 

⎜ 

⎜ 

⎝ 

⎞ 

⎟ 

⎟ 

⎟ 

⎠ 

back



Issue: 1.3 

Date: 28/01/04 

Page: 48/14 


( ( ) EE spa λ ) 


AM 

( ) 

K Sky ( λ) := K S k spec ⋅∆ spec , k spa_good ⋅∆ WFspa , λ 

K RN 

K DC 

2 

:= k spa_good ⋅ kspec 

:= K RN 

( ) 

LimFLineWFnoao good ( λ) := F O SN lim , n exp , t exp , F Sky ( λ) 


, K Sky ( λ) 

, K RN , K DC 

1 . 10 18 

LimFLineWFnoao good 

( λ) 

erg⋅s − 1 ⋅cm − 2 

LimVFLineWFnoao goodi 

i := 0.. 

4 

0.4 0.5 0.6 0.7 0.8 0.9 1 

:= LimFLineWFnoao good ⎛ λ 

⎝ MUSEi ⎞ 

⎠ 

⎛ 

⎜ 

⎜ 

1.503× 

10 − 18 

5.7 × 10 − 19 

⎜ 

LimVFLineWFnoao good = ⎜ 3.875× 

10 − 19 

⎜ 

⎜ 

⎜ 

⎝ 

3.563× 

10 − 19 

4.693× 

10 − 19 

⎞ 

⎟ 

⎟ 

⎟ 

⎟ 

⎟ 

⎠ 

erg⋅s − 1 ⋅cm − 2 

λ 

µm 

Band MUSE = 

⎛ 

⎜ 

⎜ 

⎜ 

⎜ 

⎜ 

⎝ 

"B" 

"V" 

"R" 

"I" 

"z" 

⎞ 

⎟ 

⎟ 

⎟ 

⎠ 

back 

( ) 

FN( λ) := FNoise LimFLineWFnoao good ( λ) , n exp , t exp , F Sky ( λ) 


, K Sky ( λ) 

, K RN , K DC 

FN( λ V ) 

⎛ 

0.012 

⎜ 

0.75 

⎜ ⎟ 

= ⎜ 0.201⎟ 

FN λ R 

⎜ 0.038⎟ 

⎜ 

0.238 

⎝ 

⎞ 

⎠ 

( ) 

⎛ 

0.012 

⎜ 

0.77 

⎜ ⎟ 

= ⎜ 0.184⎟ 

FN λ z 

⎜ 0.034⎟ 

⎜ 

0.218 

⎝ 

⎞ 

⎠ 

( ) 

= 

⎛ 

⎜ 

⎜ 

⎜ 

⎜ 

⎜ 

⎝ 

0.017 

0.535 

0.378 

0.071 

0.448 

⎞ 

⎟ 

⎟ 

⎟ 

⎠ 

back



Issue: 1.3 

Date: 28/01/04 

Page: 49/14 

15.1.3 AO Gen I, poor seeing conditions 

i := 0.. 

4 

( ) 

EE spa ( λ) := EEgenI poor λ , k spa_poor 

0.7 

0.6 

EE spa 

( λ) 

0.5 

0.4 

0.3 

0.4 0.5 0.6 0.7 0.8 0.9 1 

λ 

µm 

.1.3.1 Continuum source 

( ) 


, ∆ spec , 1 , λ, 

AM 

( ) 

K Sky ( λ) := K S ∆ spec , k spa_poor ⋅∆ WFspa , λ 

back 

2 

K RN := k spa_poor 

2 

K DC := k spa_poor 

( ) 

LimFContWFgenI poor ( λ) := F O SN lim , n exp , t exp , F Sky ( λ) 


, K Sky ( λ) 

, K RN , K DC 

5 .10 19 

LimFContWFgenI poor 

( λ) 

3 .10 19 

erg⋅s − 1 ⋅cm − 2 ⋅A − 1 

1 .10 19 

0.4 0.5 0.6 0.7 0.8 0.9 1 

λ 

µm

⎛ 

LimMagContWFgenI poori := Flux2AB LimFContWFgenI poor λ MUSEi , 


⎝ 

i 

i := 0.. 

4 

⎛ 

⎜ 

24.261 

"B" 

⎜ 

25.11 

"V" 

⎜ ⎟ 

⎜ 

LimMagContWFgenI poor = ⎜ 25.223⎟ 


⎜ 24.87 ⎟ 

"I" 

⎜ 

24.257 

"z" 



, ∆ lowspec , 1 , λ, 

AM 

⎝ 

( ) 

( ) 

K Sky ( λ) := K S ∆ lowspec , k spa_poor ⋅∆ WFspa , λ 

2 

K RN := N sumspec k spa_poor 

⎞ 

⎠ 



Issue: 1.3 

Date: 28/01/04 

Page: 50/14 

⎛ 

⎛ 

⎜ 

⎜ 

⎝ 

⎞ 

⎞ 

⎟ 

⎟ 

⎟ 

⎠ 

⎞ 

⎠ 

K DC 

:= K RN 

( ) 

LimFContWFgenI poor ( λ) := F O SN lim , n exp , t exp , F Sky ( λ) 


, K Sky ( λ) 

, K RN , K DC 

⎛ 

LimMagContLowWFgenI poori := Flux2AB LimFContWFgenI poor λ 

⎝ MUSEi , 

⎠ λ MUSE 

⎝ 

i 

i := 0.. 

4 

⎛ 

25.52 

"B" 

⎜ 

⎜ 

26.366 

"V" 

⎜ ⎟ 

⎜ 

LimMagContLowWFgenI poor = ⎜ 26.479⎟ 


⎜ 26.126⎟ 

"I" 

⎜ 

25.515 

"z" 

⎝ 

⎞ 

⎠ 

⎛ 

⎛ 

⎜ 

⎜ 

⎝ 

⎞ 

⎞ 

⎟ 

⎟ 

⎟ 

⎠ 

⎞ 

⎠



Issue: 1.3 

Date: 28/01/04 

Page: 51/14 


( ( ) EE spa λ ) 


AM 

( ) 

K Sky ( λ) := K S k spec ⋅∆ spec , k spa_poor ⋅∆ WFspa , λ 

K RN 

K DC 

2 

:= k spa_poor ⋅ kspec 

:= K RN 

( ) 

LimFLineWFgenI poor ( λ) := F O SN lim , n exp , t exp , F Sky ( λ) 


, K Sky ( λ) 

, K RN , K DC 

2 . 10 18 

1.37 . 10 18 

LimFLineWFgenI poor 

( λ) 

erg⋅s − 1 ⋅cm − 2 

7.33 . 10 19 

1 .10 19 

0.4 0.5 0.6 0.7 0.8 0.9 1 

λ 

µm 

LimVFLineWFgenI poori 

:= LimFLineWFgenI poor ⎛ λ MUSEi ⎞ 

⎝ ⎠ 

i := 0.. 

4 

⎛ 

⎜ 

2.109× 

10 − 18 

⎛ "B" 

⎜ 6.908× 

10 − 19 ⎟ 

⎜ 

"V" 

⎜ 

⎟ 

LimVFLineWFgenI poor ⎜ 4.596× 

10 − 19 ⎟ erg⋅s − 1 cm − 2 

⎜ 

= ⋅ 


⎜ 

⎟ 

⎜ 4.177× 

10 − 19 

⎜ "I" 

⎟ 

⎜ 

⎜ 

5.293× 

10 − 19 

⎝ "z" 

⎝ 

⎞ 

⎠ 

⎞ 

⎟ 

⎟ 

⎟ 

⎠ 

back 

( ) 

FN( λ) := FNoise LimFLineWFgenI poor ( λ) , n exp , t exp , F Sky ( λ) 


, K Sky ( λ) 

, K RN , K DC 

FN( λ V ) 

⎛ 

⎜ 

⎜ 

⎜ 

⎜ 

⎜ 

⎜ 

⎝ 

9.05× 

10 − 3 

0.752 

⎞ 

⎟ 

⎟ 

= 0.201 

FN λ 

⎟ 

R 

0.038 ⎟ 

0.239 

⎠ 

( ) 

⎛ 

⎜ 

⎜ 

⎜ 

⎜ 

⎜ 

⎜ 

⎝ 

8.661× 

10 − 3 

0.772 

= 0.184 

FN λ 

⎟ 

z 

0.035 ⎟ 

0.219 

⎞ 

⎟ 

⎟ 

⎠ 

( ) 

= 

⎛ 

⎜ 

⎜ 

⎜ 

⎜ 

⎜ 

⎝ 

0.012 

0.537 

0.379 

0.071 

0.45 

⎞ 

⎟ 

⎟ 

⎟ 

⎠ 

back



Issue: 1.3 

Date: 28/01/04 

Page: 52/14 

15.1.4 AO Gen I, good seeing conditions 

back 

i := 0.. 

4 

( ) 

EE spa ( λ) := EEgenI good λ , k spa_good 

0.7 

EE spa 

( λ) 

0.6 

0.5 

0.4 0.5 0.6 0.7 0.8 0.9 1 

λ 

µm 


back 

( ) 


, ∆ spec , 1 , λ, 

AM 

( ) 

K Sky ( λ) := K S ∆ spec , k spa_good ⋅∆ WFspa , λ 

2 

K RN := k spa_good 

2 


( ) 

LimFContWFgenI good ( λ) := F O SN lim , n exp , t exp , F Sky ( λ) 


, K Sky ( λ) 

, K RN , K DC 

4 .10 19 

LimFContWFgenI 

3 .10 19 

good 

( λ) 

erg⋅s − 1 ⋅cm − 2 ⋅A − 1 

2 .10 19 

1 .10 19 

0.4 0.5 0.6 0.7 0.8 0.9 1 

λ 

µm 

⎛ 

LimMagContWFgenI goodi := Flux2AB LimFContWFgenI good λ MUSEi , 


⎝ 

i 

⎛ 

⎞ 

⎞ 

⎠



Issue: 1.3 

Date: 28/01/04 

Page: 53/14 

i := 0.. 

4 

⎛ 

24.911 

"B" 

⎜ 

⎜ 

25.608 

"V" 

⎜ ⎟ 

⎜ 

LimMagContWFgenI good = ⎜ 25.711⎟ 


⎜ 25.345⎟ 

"I" 

⎜ 

24.697 

"z" 



, ∆ lowspec , 1 , λ, 

AM 

⎝ 

( ) 

( ) 

K Sky ( λ) := K S ∆ lowspec , k spa_good ⋅∆ WFspa , λ 

2 


⎞ 

⎠ 

⎛ 

⎜ 

⎜ 

⎝ 

⎞ 

⎟ 

⎟ 

⎟ 

⎠ 

K DC 

:= K RN 

( ) 

LimFContWFgenI good ( λ) := F O SN lim , n exp , t exp , F Sky ( λ) 


, K Sky ( λ) 

, K RN , K DC 

⎛ 

LimMagContLowWFgenI goodi := Flux2AB LimFContWFgenI good λ 

⎝ MUSEi , 

⎠ λ MUSE 

⎝ 

i 

i := 0.. 

4 

⎛ 

⎞ 

⎞ 

⎠ 

⎛ 

26.172 

"B" 

⎜ 

⎜ 

26.866 

"V" 

⎜ ⎟ 

⎜ 

LimMagContLowWFgenI good = ⎜ 26.968⎟ 


⎜ 26.602⎟ 

"I" 

⎜ 

25.958 

"z" 

⎝ 

⎞ 

⎠ 

⎛ 

⎜ 

⎜ 

⎝ 

⎞ 

⎟ 

⎟ 

⎟ 

⎠ 

back



Issue: 1.3 

Date: 28/01/04 

Page: 54/14 


( ( ) EE spa λ ) 


AM 

( ) 

K Sky ( λ) := K S k spec ⋅∆ spec , k spa_good ⋅∆ WFspa , λ 

K RN 

K DC 

2 


:= K RN 


( ) 

LimFLineWFgenI good ( λ) := F O SN lim , n exp , t exp , F Sky ( λ) 


, K Sky ( λ) 

, K RN , K DC 

1 . 10 18 

LimFLineWFgenI good 

( λ) 

5.5 . 10 19 

erg⋅s − 1 ⋅cm − 2 

1 .10 19 

0.4 0.5 0.6 0.7 0.8 0.9 1 

λ 

µm 

LimVFLineWFgenI goodi 

i := 0.. 

4 

:= LimFLineWFgenI good ⎛ λ 

⎝ MUSEi ⎞ 

⎠ 

⎛ 

⎜ 

⎜ 

1.157× 

10 − 18 

4.36× 

10 − 19 

⎜ 

LimVFLineWFgenI good = ⎜ 2.93× 

10 − 19 

⎜ 

⎜ 

⎜ 

⎝ 

2.695× 

10 − 19 

3.523× 

10 − 19 

⎞ 

⎟ 

⎟ 

⎟ 

⎟ 

⎟ 

⎠ 

erg⋅s − 1 ⋅cm − 2 

Band MUSE = 

⎛ 

⎜ 

⎜ 

⎜ 

⎜ 

⎜ 

⎝ 

"B" 

"V" 

"R" 

"I" 

"z" 

⎞ 

⎟ 

⎟ 

⎟ 

⎠ 

back 

( ) 

FN( λ) := FNoise LimFLineWFgenI good ( λ) , n exp , t exp , F Sky ( λ) 


, K Sky ( λ) 

, K RN , K DC 

FN( λ V ) 

⎛ 

0.012 

⎜ 

0.75 

⎜ ⎟ 

= ⎜ 0.201⎟ 

FN λ R 

⎜ 0.038⎟ 

⎜ 

0.238 

⎝ 

⎞ 

⎠ 

( ) 

⎛ 

0.012 

⎜ 

0.77 

⎜ ⎟ 

= ⎜ 0.184⎟ 

FN λ z 

⎜ 0.034⎟ 

⎜ 

0.218 

⎝ 

⎞ 

⎠ 

( ) 

= 

⎛ 

⎜ 

⎜ 

⎜ 

⎜ 

⎜ 

⎝ 

0.017 

0.535 

0.378 

0.071 

0.448 

⎞ 

⎟ 

⎟ 

⎟ 

⎠ 

back



Issue: 1.3 

Date: 28/01/04 

Page: 55/14 

15.2 HR mode 

i := 0.. 

4 

EE spa ( λ) := EEgenII good λ , k spa_good 

15.2.1 AO Gen II, good seeing conditions 

( ) 

back 

0.6 

EE spa 

( λ) 

0.4 

0.2 

0.4 0.5 0.6 0.7 0.8 0.9 1 

λ 

µm 


( ) 


, ∆ spec , 1 , λ, 

AM 

back 

K RN k spa_good 

2 

:= 

2 


( ) 

LimFContHRgenII good ( λ) := F O SN lim , 1, t exp , F Sky ( λ) 


, K Sky ( λ) 

, K RN , K DC 

LimFContHRgenII 

7 .10 18 

good 

( λ) 

erg⋅s − 1 ⋅cm − 2 ⋅A − 1 

4 . 10 18 

1 .10 18 

0.4 0.5 0.6 0.7 0.8 0.9 1 

λ 

µm



Issue: 1.3 

Date: 28/01/04 

Page: 56/14 

⎛ 

LimMagContHRgenII goodi := Flux2AB LimFContHRgenII good λ MUSEi , 


⎝ 

i 

i := 0.. 

4 

⎛ 

21.084 

"B" 

⎜ 

⎜ 

21.994 

"V" 

⎜ ⎟ 

⎜ 

LimMagContHRgenII good = ⎜ 22.3 ⎟ 


⎜ 22.09 ⎟ 

"I" 

⎜ 

21.657 

"z" 



, ∆ lowspec , 1 , λ, 

AM 

⎝ 

( ) 

( ) 

K Sky ( λ) := K S ∆ lowspec , k spa_good ⋅∆ HRspa , λ 

2 


⎞ 

⎠ 

⎛ 

⎛ 

⎜ 

⎜ 

⎝ 

⎞ 

⎞ 

⎟ 

⎟ 

⎟ 

⎠ 

⎞ 

⎠ 

K DC 

:= K RN 

( ) 

LimFContHRgenII good ( λ) := F O SN lim , 1, t exp , F Sky ( λ) 


, K Sky ( λ) 

, K RN , K DC 

⎛ 

LimMagContLowHRgenII goodi := Flux2AB LimFContHRgenII good λ MUSEi , 


⎝ 

i 

i := 0.. 

4 

⎛ 

⎞ 

⎞ 

⎠ 

⎛ 

23.065 

"B" 

⎜ 

⎜ 

24.491 

"V" 

⎜ ⎟ 

⎜ 

LimMagContLowHRgenII good = ⎜ 24.848⎟ 


⎜ 24.608⎟ 

"I" 

⎜ 

23.755 

"z" 

⎝ 

⎞ 

⎠ 

⎛ 

⎜ 

⎜ 

⎝ 

⎞ 

⎟ 

⎟ 

⎟ 

⎠ 

back



Issue: 1.3 

Date: 28/01/04 

Page: 57/14 


( ( ) EE spa λ ) 


AM 

K RN 

K DC 

2 


:= K RN 


( ) 

LimFLineHRgenII good ( λ) := F O SN lim , 1, t exp , F Sky ( λ) 


, K Sky ( λ) 

, K RN , K DC 

2 .10 17 

LimFLineHRgenII 

1.37 .10 17 

good 

( λ) 

erg⋅s − 1 ⋅cm − 2 

7.33 . 10 18 

1 .10 18 

0.4 0.5 0.6 0.7 0.8 0.9 1 

λ 

µm 

LimVFLineHRgenII goodi 

i := 0.. 

4 

:= LimFLineHRgenII good ⎛ λ 

⎝ MUSEi ⎞ 

⎠ 

⎛ 

⎜ 

2.148× 

10 − 17 

⎛ "B" 

⎜ 4.261× 

10 − 18 ⎟ 

⎜ 

"V" 

⎜ 

⎟ 

LimVFLineHRgenII good ⎜ 2.277× 

10 − 18 ⎟ erg⋅s − 1 cm − 2 

⎜ 

= ⋅ 


⎜ 

⎟ 

⎜ 1.858× 

10 − 18 

⎜ "I" 

⎟ 

⎜ 

⎜ 

2.857× 

10 − 18 

⎝ "z" 

⎝ 

⎞ 

⎠ 

( ) 

FN( λ) := FNoise LimFLineHRgenII good ( λ) , 1, t exp , F Sky ( λ) 


, K Sky ( λ) 

, K RN , K DC 

⎞ 

⎟ 

⎟ 

⎟ 

⎠ 

back 

FN( λ V ) 

⎛ 

0.185 

⎜ 

0.115 

⎜ ⎟ 

= ⎜ 0.59 ⎟ 

⎜ 0.111⎟ 

FN λ R 

0.7 

⎜ 

⎝ 

⎞ 

⎠ 

( ) 

⎛ 

0.183 

⎜ 

0.127 

⎜ ⎟ 

= ⎜ 0.581⎟ 

⎜ 0.109⎟ 

FN λ z 

0.69 

⎜ 

⎝ 

⎞ 

⎠ 

( ) 

= 

⎛ 

⎜ 

⎜ 

⎜ 

⎜ 

⎜ 

⎝ 

0.192 

0.047 

0.64 

0.12 

0.76 

⎞ 

⎟ 

⎟ 

⎟ 

⎠ 

back



Issue: 1.3 

Date: 28/01/04 

Page: 58/14 

16. Accuracy requirements in sky subtraction 

We compute the ratio of sky flux (outside OH lines) with the object flux 

RatioSkyObj := i 

Flux SkyNoOH ⎛ λ MUSEi 

⎝ 

⎞ 

( ) 2 

⋅ 

⎠ k spa_good⋅ 

∆ WFspa 

LimFLineWFgenI good ⎛ λ MUSEi ⎞ 

⎝ 

⎠ 

⋅k spec ⋅∆ spec 

⎛ 

10.487 

"B" 

⎜ 

⎜ 

29.526 

"V" 

⎜ ⎟ 

⎜ 

RatioSkyObj = ⎜ 34.47 ⎟ 


⎜ 37.174⎟ 

"I" 

⎜ 

20.095 

"z" 

⎝ 

⎞ 

⎠ 

⎛ 

⎜ 

⎜ 

⎝ 

⎞ 

⎟ 

⎟ 

⎟ 

⎠ 

Thus at most the sky is 40 times the object flux and sky subtraction to a precision of 1% should be 

OK in all cases.

Science preparation 

and key personnel 

Written by : R. Bacon 



Issue : 1.0 

Date : 2/02/04 

File : 

science_team.doc 

Distribution : Consortium 

History: 

• 0.1 – 25/01/04 – Initial version 

• 0.2- 28/01/04 – First release 

• 1.0 - 2/02/04 – Edit by R. McDermid, final release for phase A

Title: Science preparation and key personnel 


Issue: 1.0 

Date: 02/02/04 

Page: 2/25 

1. Documents.......................................................................................................................... 3 

1.1. Applicable documents ................................................................................................ 3 

1.2. Reference documents ................................................................................................. 3 

2. Acronyms ........................................................................................................................... 3 

3. Introduction ........................................................................................................................ 3 

4. Preparatory science ............................................................................................................ 4 

4.1. Simulations and modeling effort................................................................................ 4 

4.2. Pre-MUSE observations............................................................................................. 5 

4.2.1. Sauron deep fields .............................................................................................. 5 

4.2.2. Searches for line emitting galaxies at z~6.......................................................... 8 

4.2.3. Future plans...................................................................................................... 10 

5. Science team organization................................................................................................ 11 

6. Science team members ..................................................................................................... 12 

7. Associate of science team members................................................................................. 12 

8. Curriculum vitae............................................................................................................... 13



Issue: 1.0 

Date: 02/02/04 

Page: 3/25 

1. Documents 

1.1. Applicable documents 

AD1 MUSE Science Case 


1.2. Reference documents 

RD1 Data reduction, quality control and quick look 

software 

RD2 Phase B&C Development & Management Plans 

RD3 Data analysis software tools 


MUSE-MEM-MAN-041 


2. Acronyms 

AD 

AO 

CCD 

ESO 

MUSE 

NA 

NFM 

PSF 

RD 

TBC 

TBD 

VLT 

WFM 



Charge-Coupled Device 






Reference Document 


To Be Defined 


Wide Field Mode 


During the seven years duration of the project, one can expect evolution in the science areas 

we have identified for MUSE. It will be the science team's responsibility to keep the science 

case updated and eventually to develop new subjects. The team will also develop simulations, 

models and theory that are needed for the science return. In addition, the team will provide a 

large effort to build comprehensive data analysis software. The latter is described in a separate 

document (RD3). We give here an overview of the preparatory science we envision and how 

we have organized ourselves to achieve these goals.

4. Preparatory science 



Issue: 1.0 

Date: 02/02/04 

Page: 4/25 

4.1. Simulations and modeling effort 

As discussed in RD2, we have planned to develop a full Instrument Numerical Model (INM). 

The INM should be able to simulate MUSE sky observations and to produce the resulting 403 

Mega-pixel raw frames. It will be the responsibility of the science team to build datacubes 

representative of the main science subjects identified in AD1. The raw exposures will then be 

processed by the data-reduction pipeline (RD1), and analyzed with the data analysis tools 

developed in RD3. Comparison with the original data will help us to measure the instrument 

performances, including non-linear effects and systematic errors. It will also be critical for 

software development and optimizing survey strategies. 

The already team has in hand some major tools to provide such data sets. For example, the 

GalIcs package, described in AD1, is able to provide observation cones of galaxies in a sky 

area at a depth compatible with the sensitivity of MUSE. We have already used this tool to 

build representative MUSE images (see Fig 1). In the context of MUSE, the capabilities of 

GalIcs are currently being expanded to produce spectra, giving continuum and nebular lines 

that are needed for the datacube construction. 

Figure 1: Two simulated MUSE deep-field images using the same GalIcs 

observation cone and two different atmospherics conditions: natural seeing in poor 

conditions (left) and AO with median seeing conditions (right). 

The team has expertise in many general modeling tools such as n-body simulations. It also has 

experience in more specific tools such as dynamical modeling using the Scharzschild orbit 

superposition technique to simulate kinematics of galaxies. 

In some cases, however, existing tools are not sufficient and specific models need to be 

developed for the science goals. This is the case for the Ly α line modeling. This line is 

particularly difficult, because of the existence of stellar absorption after a starburst (due to A 

stars), and of resonant scattering. In principle, only a small fraction of the emitted Ly α 

photons should escape from a dusty medium. Nevertheless, Ly α is still observed in local



Issue: 1.0 

Date: 02/02/04 

Page: 5/25 

objects (such a Blue Compact Dwarfs) and high-redshift galaxies. It is thought that the 

existence of an expanding medium prevents Ly α photons from resonant scattering, and 

produces the characteristic P-Cygni profiles. There is no simple modeling of such a process. 

At present, we make predictions for two basic models: (i) transfer in a dusty medium without 

resonant scattering (for example, if the velocity of the expanding medium is high enough to 

completely hamper resonant scattering); (ii) a fixed escape fraction (which would correspond 

to "holes" in the gas and dust distribution). A more realistic model is clearly needed, and the 

team plans to make progress in that field, with the help of expertise in transfer models from 

the community. 

4.2. Pre-MUSE observations 

4.2.1. Sauron deep fields 

In preparation for the deep surveys planned with MUSE, a pilot programme has been 

developed using the SAURON IFU spectrograph (Bacon et al. 2001). We have now surveyed 

three fields, with a paper describing the first of these now in press (R. G. Bower, S.L. Morris, 

R. Bacon, R. J. Wilman, M. Sullivan, S. Chapman, R.L. Davies, P.T. de Zeeuw, E. Emsellem 

MNRAS). Here we briefly present some 

relevant details from this paper. 

The target of the observations was a large 

scale, highly luminous Ly-α halo, found by 

Steidel et al., (hereafter LAB1, figure 2). The 

target is the brightest halo in the conspicuous 

SSA22 super-cluster at z = 3.07 − 3.11 

(Steidel et al., 2000). The highly-obscured, 

very luminous sub-millimetre galaxy found by 

SCUBA near the centre of this halo (SMM 

J221726+0013, Chapman et al., 2001) is 

possibly a massive elliptical galaxy seen in 

formation (Eales et al., 1999, Smail et al., 

2002). Using SAURON, we can map the 

emission line profiles across the LAB1 

structure. This allows us to probe the nature of 

the ionised gas surrounding the SCUBA 

source, gaining insight into the origin of the 

diffuse halo (is it primordial material infalling 

onto the central object, or material expelled 

during a violent star burst?), the mass of its 

dark matter halo, and the energetics of any 

super-wind being expelled from the galaxy. 

We can also trace the large-scale structure 

surrounding the central source, and investigate 

whether similar haloes surround other galaxies 

in the field. Throughout, we assume a flat 

cosmology with H 0 = 70 km s−1 Mpc −1 , Ω= 

0.3 and Λ = 0.7. This gives an angular scale at 

z = 3.1 of 7.5 kpc/arcsec. 

Figure 2. A deep STIS image of the SSA22 

LAB1 region showing the position for the 

SCUBA counterpart (Chapman et al., 2003) 

relative to the total Ly-α emission (contours). 

The sub-mm source may lie in a 3-D cavity in 

the emission. The Lyman-break galaxies C15 

and C11 are marked: their distinct haloes 

are clearly seen in the 3-D data set.

The SAURON instrument 

combines wide-field (41 arcsec × 

33 arcsec sampled at 0.95 arcsec) 

with a relatively high spectral 

resolution (4 Å FWHM, equivalent 

to σ = 100 km s −1 in the target rest 

frame). The instrument achieves 

this by compromising on the total 

wavelength coverage, which is 

limited to the range from 4810 to 

5400 Å. This spatial and spectral 

sampling ensures that low surface 

brightness features are not 

swamped by read-out noise. 

SAURON was used to observe the 

SSA22 source for a total of 9 

hours, spread over 3 nights in July 



Issue: 1.0 

Date: 02/02/04 

Page: 6/25 

Figure 3. Single Gaussian fits to the data. Panel (a) shows 

the intensity of the fitted line (0-2 × 10 −17 erg s −1 cm −2 per 

sq. arcsec); Panel (b), the central wavelength of the line 

(4973-4992 Å); Panel (c), the width of the line (σ = 0-15 Å). 

The plots allow us to quantify the velocity structure seen in 

the halo. 

2002. The raw data were reduced using the XSauron software. The end result is a 3-D (x,y,λ) 

map of the Ly α emission from the region, each spectral-pixel has a size of 1 arcsec in the 

spatial dimensions and a size of 1.15 Å in the wavelength dimension. 

To quantify the emission and its spatial variations, we fitted each spectrum with a single 

Gaussian line of variable position, width and normalisation. The best fitting parameters for 

each lenslet are shown in Figure 3. The limiting surface brightness at which we were able to 

reliably detect and fit to the line is a function of the line width was found to be from 1×10 −18 

erg s −1 cm −2 per sq. arcsec for lines with σ = 2 Å, to 3.5×10 −18 erg s −1 cm −2 per sq. arcsec for 

lines with σ = 20 Å. 

The emission halo can be traced out to almost 100 kpc from the sub-millimetre source, and 

the two nearby Lyman-break galaxies are shown to have kinematically distinct emission line 

haloes of their own. The main features that we can discern are: 

• The emission line profile around the central sub-mm source is broad, σ~8 Å. 

• While the line profile varies significantly around the sub-mm source, there is no coherent 

variation in the line centroid. 

• Ly α emission appears suppressed in the immediate vicinity of the sub-mm source. 

• The Ly-break galaxies C15 and C11 appear to be associated with enhancements in the 

emission. These “mini-haloes” show significant velocity shear.

If we interpret the broad width of the 

emission line as being due to velocity 

motion of individual gas clouds, we 

infer line of sight velocities of ~ 500 

km s −1 , suggesting a dark halo mass of 

1.3 × 10 13 M , as expected for a small 

cluster. We compare the emission halo 

to the emission filaments surrounding 

NGC 1275, the central galaxy of the 

Perseus cluster. The chaotic velocity 

structure and the extent of the emission 

are similar, although the Ly α 

luminosity of LAB1 is two orders of 

magnitude larger. Combined with the 

lack of coherent velocity shear and the 

high ratio of the Ly α and X-ray flux, 

the comparison leads us to speculate 

that the emission halo of SMM 

J221726+0013 is powered by the 

interaction between cooling gas and a 

relatively weak out-flow from the 

central source. Our data do not 

distinguish whether this flow is driven 

by vigorous star formation or by a 

heavily obscured AGN. 



Issue: 1.0 

Date: 02/02/04 

Page: 7/25 

Figure 4 SAURON cube summed in the 

wavelength direction to produce a continuum 

image, showing the QSO in the centre and 

some foreground stars. 

It is clear, however, that this 

interpretation needs to be confirmed by 

combining radiative transfer models with 

realistic simulations of massive galaxy 

formation in the early universe. The 

structure of emission halo suggests a 

cavity around SMM J221726+0013. 

While one possible explanation is that 

this region has been filled with hot, 

completely ionised material, the dip in 

the emission may equally be explained 

because of dust obscuration in the 

material ejected from the sub-millimeter 

source. 

The “mini-haloes” around the two 

Lyman-break galaxies in the field (C11 

and C15) show clear velocity shear 

across their emission haloes. The 

structure appears to be consistent with a 

bipolar outflow of material, similar to 

Figure 5. Red dots indicate ‘detections’ of line 

emitting objects. For reference, continuum 

objects visible in figure 4 above are marked with 

dotted lines along the wavelength direction. 

Some of the detections are hence the residuals 

from the continuum subtraction process. 

Nevertheless, there are a number of other strong 

detections.



Issue: 1.0 

Date: 02/02/04 

Page: 8/25 

that seen in the star-bursting dwarf galaxy M82. If the material is an out-flow, the deprojected 

velocity of the flow is ~200 km s −1 : less than the velocity inferred for the outflow from M82, 

and less than the outflow velocities inferred by Pettini et al. (1998, see also Teplitz et al., 

2000) from comparison of the redshifts of Ly α and nebular emission lines in the rest-frame 

optical. 

Further Observations 

We have also observed a field centred on a bright QSO (HB89-1738+350). This is a V=20.5, 

z=3.239 QSO chosen so that its rest frame Ly α would be inside the SAURON range, but also 

so that the cube would include a significant range where intervening absorption systems seen 

along the line of sight to the QSO could be correlated with any emission line objects found in 

the SAURON cube. 

The analysis of this data set (figure 4) is still in progress, but preliminary results from work by 

Joris Gersson are shown in figure 5. The detection algorithm is currently under development, 

but follows the traditional approach of searching for single pixels with significant flux in 

them, and then requiring that a certain number of nearby pixels are also above some (lower) 

threshold. This process is run twice – once searching for positive detections, and once for 

negative detections to allow an estimate to be made of the number of spurious detections. 

References 

Bacon et al., 2001, MNRAS, 326, 23 

Chapman, Lewis, Scott, et al., 2001, ApJ, 548, 17 

Eales et al., 1999, APJ, 515, 518 

Pettini M., Kellog M., Steidel C. S., Dickinson M., Adelberger K., Giavalisco M., 1998, 508, 

539 

Smail I., Ivison R., Blain W.A., Kneib J.-P., 2002, MNRAS, 331, 495 

Steidel, Adelberger, Shapley, Pettini, Dickinson, Giavalisco, 2000, ApJ ,532, 170 

Teplitz H.I., McLean I.S., Becklin E.E., 2000, ApJ, 533, L63 

4.2.2. Searches for line emitting galaxies at z~6. 

Using existing multi-object spectrographs (e.g. FORS-2) in a multi-slit+filter mode 

(Crampton and Lilly 1999) enables us to carry out "blank-field" searches for line emission 

that are directly comparable to the integral field approach of MUSE. In these surveys, the 

integral field is effectively built up out of multiple long-slit exposures displaced in position. 

The much smaller number of pixels in FORS-2 relative to MUSE means that such a survey 

must necessarily be more limited in spatial coverage, spectral range, and/or spatial and 

spectral resolution. The FORS-2 survey being undertaken as MUSE-precursor science is only 

targeted at the 9000-9250 Å atmospheric window between the OH forest, which corresponds 

to 6.42 < z < 6.58 for Ly α . This is a particularly interesting range, since it represents the 

highest redshifts known. Furthermore, the evidence that the reionization of the Universe may



Issue: 1.0 

Date: 02/02/04 

Page: 9/25 

have been completed by z ~ 6 makes the detection and study of all objects at these redshifts 

extremely interesting, since they may well be representative of the objects that were 

responsible for this process. 

We have carried out exploratory observations with FORS-2 in P71 that demonstrated the 

feasibility of this technique on the VLT. With the chosen observational set-up, we obtain a 

limiting line flux in normal 0.8 arcsec seeing of 3 × 10 -18 erg s -1 cm -2 (5σ in 1.6 arcsec 

aperture) in 8.1 ksec over an area of 2.1 arcmin 2 (consisting of nine parallel slits traversing a 6 

× 7 arcmin 2 area). 

We have proposed a major survey of the GOODS-S and HDF-S regions for P73/74, the 

results of which are still pending. In this proposed survey, 26 individual pointings in each 

region enable a contiguous field to be built up (whereas a true integral field spectrograph such 

as MUSE obtains such a contiguous field at each pointing). 

Table 1 compares the parameters of the proposed FORS-2 survey (58 hrs per field) with a 60 

hr exposure with MUSE. 

Table 1 – Figures of merit of FORS-2 multi-slit & MUSE Ly α search. 

FORS-2 multi-slit+mask MUSE 

VLT observation time 58 hrs 60 hrs 

Area on sky 42 arcmin 2 1 arcmin 2 

Redshift interval 6.43−6.58 2.8−6.6 

Line sensitivity (5σ) 3 × 10 -18 erg s -1 cm -2 (0.8" 3.7−7.5 × 10 -19 erg s -1 

seeing) 

cm -2 (various modes) 

Spectral resolution 1800 4200 

Figure of merit 1 90−20 

Figure of merit z > 5 1 38−8 

The FORS-2 survey is designed to determine the luminosity function of Ly α emitting galaxies 

at z ~ 6.5 over the interval 10 42 < L(Ly α ) < 10 43 (see Fig 6). The lower bound is a factor of 

several fainter than other objects known at this redshift, even allowing for the magnification, 

where appropriate, of foreground gravitational lenses. Assembly of the FORS-2 data into a 

true MUSE-like data-cube will enable us to search for large diffuse Ly α emission that extends 

over many arcseconds. 

Related to both this program and MUSE itself, we are also undertaking a study of Ly α 

radiative transfer in the Universe around the epoch of recombination in order to understand 

the expected appearance of the diffuse intergalactic medium.



Issue: 1.0 

Date: 02/02/04 

Page: 10/25 

Fig 6. (left) Observational estimates of the cumulative luminosity function of candidate Ly α emitters 

(LAE) at z ~ 5.8. The three continuous functions are from (l-r) the LALA survey [19], the Subara 

Deep Survey [1] and the CADIS survey [18]. LALA only picks up LAE of very high equivalent width 

but has a high confirmation rate so far [19], so it is likely a lower limit. CADIS has a lower 

confirmation rate and may be regarded as an upper limit. The three points separated by a dotted 

line is the sample of 15 objects from Hu et al (2003). The right-angled limits represents the lack of 

detection by Martin & Sawicki using a similar but less sensitive technique to that used by us. The 

hatched area is a reasonable representation of these observations with reality likely lying in the 

middle. (right) As at left, except at z = 6.5. The hatched region is simply translated from the z = 5.8 

diagram assuming no evolution. The upper and far-left axes show observational quantities. Only 

three galaxies are known at this redshift, those from Hu et al (H02) and Kodaira et al (K03) The 

former is shown both as observed and also demagnified by the A370 lens. The variable sensitivity of 

the proposed survey arising from sky lines and spatial effects due to the VPH grating gives the 

curved sensitivity-area relation shown as the heavy line. We expect to detect 20 LAE down to a 

sensitivity limit substantially below that explored by the previous narrow-band surveys. Adapting 

the number density from Hu et al would boost this by an order of magnitude. 

4.2.3. Future plans 

We plan to continue and extend these 

observations in the context of MUSE science 

preparation. For example, a third SAURON 

deep field has recently been obtained on another 

region of the SSA cluster. The 20 hours of 

integration on this field would allow us to get 

deeper and to improve our experience in datareduction 

of IFUs in this context. Along the 

same line, we plan to use the PMAS IFU (Roth 

et al, 2000) in operation at Calar Alto 3.5m 

telescope. The recently commissioned new 

PPAK fiber bundle gives a large field of view 

(70x70 arcsec²) at the cost of low spatial 

sampling (2.7 arcsec). An example of PMAS 

capabilities is shown in Figure 7. 

Fig.7. Ly α contours of the DLA galaxy on the lineof-sight 

to Q2233+131 as observed with PMAS at 

the Calar Alto 3.5m Telescope, superimposed on 

an 8” × 8” WFPC2 image. The total Ly α flux 

measured from the 2 hours PMAS exposure is 

2.4×10 -16 erg/cm 2 /sec with a 3σ detection limit of 

1×10 -17 erg/cm 2 /sec. The inferred velocity field is 

inconsistent with rotation of the DLA galaxy and 

interpreted as an outflow. From Christensen et 

al.2004, A&A in press.

5. Science team organization 



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Science is organized around a science team with full membership and associates. Five 

instrument scientists have key roles in science, AO, AIT and software areas. Decisions belong 

to the executive board. A detailed description of the various roles and responsibilities is given 

below. 

• Executive board (6 members). This is the unique body for main decisions on the 

project, including science management. It brings together the 5 CoIs (one per 

consortium institute) and the PI. In the science area, it will define science priorities, 

use of the guaranteed time, science team and associate membership, and authorship of 

papers. When discussing science matters, the board may be assisted by the instrument 

scientists. 

• Instrument scientists (5 members). Instrument scientists (IS) are responsible for 

keeping the link between science and the instrument. They shall keep the science team 

updated with the instrument development. They are consulted by the PI for all matters 

related to instrument trade-offs. We have defined 5 positions: a main instrument 

scientist and his deputy, an AO, an AIT and a Software instrument scientists. The AO 

IS will have to make the link between AO development and the science group. The 

AIT IS will have to ensure that AIT procedures are in agreement with instrument 

requirements and science objectives. The software IS role is to guarantee that software 

developments stay in phase with the science goals. 

• Science team (30 members). The science team is responsible to keep the science upto-date 

with the instrument development. It will propose the use of guaranteed time to 

the executive board. The science team is also in charge of survey preparation, pre- 

MUSE science, development of data analysis software (DAS), and models and 

theoretical analysis. Science team members are preferably from one of the 5 

consortium institutes, but scientists from external institutes have been appointed where 

certain expertise was missing. Science team membership is the responsibility of the 

executive board. 

• Associates to the science team (5 members). Associates to the science team are 

scientists that have a specific and part-time involvement in the project. They usually 

are working on a specific subset of the science. Associates can potentially become full 

science team members (and vice versa, science team members can step back as 

associates). The executive board is responsible for all decision related to associate 

membership.

6. Science team members 



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Surname Name Institute Resp. 

Roland Bacon Lyon PI 

Richard Bower Durham 

Bernhard Brandl Leiden 

Sylvie Cabrit Paris 

Françoise Combes Paris 

Marcella Carollo Zurich 

Hélène Courtois Lyon 

Gavin Dalton Oxford 

Roger Davies Oxford CoI 

Eric Emsellem Lyon 

Pierre Ferruit Lyon 

Olivier Le Fevre Marseille 

Marijn Franx Leiden 

Gerry Gilmore Cambridge 

Bruno Guiderdoni Lyon CoI 

Simon Lilly Zurich CoI 

Richard McDermid Leiden Deputy IS 

Simon Morris Durham 

Emmanuel Pécontal Lyon AIT IS 

Patrick Pinet Toulouse 

Andreas Quirrenbach Leiden AO IS 

Martin Roth Potsdam Software IS 

Sebastian Sanchez Potsdam 

Matthias Steinmetz Potsdam CoI 

TBD 

Oxford 

TBD 

Zurich 

TBD 

Zurich 

Niranjan Thatte Oxford IS 

Lutz Wisotzki Potsdam 

Tim de Zeeuw Leiden CoI 

7. Associate of science team members 

Surname Name Institute 

Katherine Blundell Oxford 

Michele Capellari Leiden 

Julien Devriendt Lyon 

Bruno Jungwiert Lyon 

Marc Verheijen Groningen 

Hervé Wozniak Lyon

8. Curriculum vitae 

We give here shorts CVs of members of the science team 



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Roland Bacon PI Lyon 

Directeur de recherche au CNRS 

Director, CRAL - Observatoire de Lyon 

Member, ESO Science and Technical committee 

Member, Space Telescope Science Institute board 

Member, Scientific Committee of the french National Program for Galaxies 

PI of TIGER, OASIS integral field spectrographs 

Co-PI of SAURON integral field spectrograph 

coI of SNIFS spectrograph and NIRSPEC/JWST phase A study 

Example of relevant publications: 

• 3D spectrography at high spatial resolution. I. Concept and realization of the integral 

field spectrograph TIGER, Bacon R et al, 1995, A&A. Supp. Ser., 113, 347 

• The SAURON project. I. The panoramic integral field spectrograph, Bacon R. et al, 

2001, MNRAS, 326, 23 

• The M31 double nucleus probed with OASIS and HST : A natural m=1 mode, Bacon 

R., Emsellem E., Combes F., Copin Y., Monnet G., Martin P., 2001, A&A, 371, 409 

• The SAURON project - II. Sample and early results, de Zeeuw, P. T.,Bureau, M, 

Emsellem, E, Bacon, R., et al, 2002, MNRAS, 329, 513 

Richard Bower Science team Durham 

Reader, Department of Physics, University of Durham 

PPARC Senior Research Fellow 

Leverhulme Research Fellow (2002-2003) 

Member,William Herschel Telescope Time Allocation Committee (1999-2003) 

Durham-PI, LDSS-2 spectrograph at the Magellan 6.5m telescope 


• What Shapes the Luminosity Function of Galaxies? Benson, A. J.; Bower, R. G.; 

Frenk, C. S.; Lacey, C. G. Baugh, C. M.; Cole, S. , 2003, ApJ, 599, 38 

• Galaxies under the Cosmic Microscope: A Gemini Multiobject Spectrograph Study of 

Lensed Disk Galaxy 289 in A2218, Swinbank, A. M., Smith, J., Bower, R. G. et al., 

2003, ApJ, 598, 162 

• Galaxy properties in low X-ray luminosity clusters at z=0.25, Balogh, M., Bower, R. 

G., Smail, I. et al., 2002, MNRAS, 337, 256 

• An H α survey of the rich cluster A 1689, Balogh, M. L., Couch, W. J., Smail, I., 

Bower, R. G., Glazebrook, K., 2002, MNRAS, 335, 10



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Page: 14/25 

Bernhard Brandl Science team Leiden 

Associate Professor, Leiden University 

Member, IRS/SPITZER instrument/science team 

Member, MIRI/JWST science team 

Co-PI of PHARO (Palomar adaptive optics camera/spectrograph) 


• SPITZER Infrared Spectrum of the Prototype Starburst Nucleus of NGC 7714, B. 

Brandl, D. Weedman, J.R. Houck et al., submitted to ApJL (2004) 

• The secrets of the nearest starburst cluster: I. VLT/ISAAC Photometry of NGC 3603, 

A. Stolte, W. Brandner, B. Brandl, H. Zinnecker, E.K. Grebel, submitted to A&A 

(2004) 

• Optimized Wide-Field Survey Telescope using Adaptive Optics, B. Brandl, R.G. 

Dekany, R. Giovanelli, SPIE, 4836, 490 (2002) 

• PHARO – the Palomar High Angular Resolution Observer, T. L. Hayward, B. 

Brandl, G. E. Gull, J. R. Houck, B. Pirger, J. Schoenwald, PASP, 113, 105 (2001) 

Sylvie Cabrit Science team Paris 

Senior Astronomer at Observatoire de Paris, LERMA 

Member of the National Council of Universities (CNU) 

Member of the Time allocation committee, Canada-France-Hawaii Telescope 


• Jets from Young Stellar Objects: Current Constraints and Challenges for the Future 

Cabrit, S. 2003, Astrophysics and Space Science, v. 287, p. 259-264: 

• "Atomic T Tauri disk winds heated by ambipolar diffusion. II. Observational tests", 

Garcia, P. J. V., Cabrit, S., Ferreira, J., & Binette, L. 2001, AA, v.377, p.609-616: 

• Dougados, C., Cabrit, S., Lavalley, C., & Menard, F. 2000, AA, v.357, p.L61-L64 : 

"T Tauri stars microjets resolved by adaptive optics" 

• "Molecular Outflows from Young Stellar Objects", Richer, J. S., Shepherd, D. S., 

Cabrit, S., Bachiller, R., & Churchwell, E. 2000, Protostars and Planets IV (Book - 

Tucson: University of Arizona Press; eds Mannings, V., Boss, A.P., Russell, S. S.), p. 

867



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Françoise Combes Science team Paris 

Senior Astronomer, Observatoire de Paris, LERMA 

Co-director of National Galaxy Program of CNRS 


• "Galaxy Evolution with ALMA", Combes F.: 2001 SF2A-highlights, p. 237, EDP- 

Sciences 

• "Molecular gas in the powerful radio galaxies 3C31 and 3C264", Lim J., Leon S., 

Combes F., Trung D.V.: 2000, Astrophys. J. 545, L93 

• "Anatomy of the counter-rotating molecular disk in the spiral NGC3593", Garcia- 

Burillo S., Sempere M., Combes F. et al.: 2000, , A and A, 363, 869 

• "Molecular Shells in Cen-A", Charmandaris V., Combes F., van der Hulst J., 2000, , 

A and A, 356, L1 

Carmen Marcella Corollo Science team Zurich 

Associate Professor, ETH Zurich 

Member of Science Oversight Committee, WFC3 Camera for HST 


• The Inner Properties of Late-Type Galaxies, Carollo, C.M., Invited Review in in `Coevolution 

of black holes and galaxies', Carnegie Observatory Astrophysics Series', vol 

1, ed. L.C. Ho (Cambridge, Cambridge University Press), 2003 

• The Metallicity of 0.5 < z < 1 Field Galaxies Carollo, C.M., Lilly, S.J. The 

Astrophysical Journal Letters, 548, L153--L157, 2001 

• VLT and HST Observations of a Candidate High Redshift Elliptical Galaxy in the 

HDF-S, Stiavelli, M., Treu, T., Carollo, C.M., et al Astronomy & Astrophysics 

Letters, 343, L25--L28, 1999 

Hélène Courtois Science team Lyon 

Assistant Professor at University of Lyon 

Responsible of the CRAL Cosmology Team 


• Courtois H., Sousbie T., Paturel G., A&A, 2004, “Maps of the Local Universe”, 

accepted 

• Wood-Vasey W.M.., Aldering G., Howell A.D., Nugent P., Perlmutter S., Quimby R., 

Antilogus P., Smadja G., Bacon R., Pecontal M., Lemmonier J.P., Pecontal A., Adam 

G., Courtois H., Copin Y., Astier P., Schahmaneche K., Pain R., Rich J., 2001 AAS 

• Di Nella-Courtois H., Lanoix P., Paturel G., 1999, MNRAS 302, 587 “Calibration of 

the Faber-Jackson relation for M31 globular clusters using Hipparcos data” 

• Vauglin I, Paturel G., Borsenberger J., Fouque P., Epchtein N., Kimmeswenger S., 

Tiphene D., Lanoix P., Courtois H., 1999 A&A Suppl.v. 135, p133 “First DENIS I- 

band extragalactic catalog”



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Gavin Dalton Science team Oxford 

Lecturer in Astrophysics - University of Oxford 

Instrument Scientist - Rutherford Appleton Laboratory 

PI of Subaru's FMOS IR multi-object spectrograph 

Instrument Scientist on VISTA IR Camera 

Member PPARC's ING board 

Member ESO Surveys Working Group 


• The 2dF Galaxy Redshift Survey: Spectra & Redshifts, Colless M.M., Dalton, G.B. et 

al. 2001, MNRAS, 328, 1039 

• The 2dF Galaxy Refshift Survey: A study of Catalogued Clusters of Galaxies, de 

Propris, R., Couch, W., Colless, M., Dalton, G.B., et al. 2002, MNRAS, 329, 87 

• Clustering of Lyman-break Galaxies in the ODT Survey: Strong Luminosity 

Dependent Bias at z=4, Allen, P., Dalton, G.B., et al., 2004, MNRAS, submitted 

• Optical Identification of the ASCA-Lynx Deep Survey, Ohta, K., Dalton, G.B., et al., 

ApJ 598, 210 

Roger Davies Col Oxford 

Philip Wetton Professor of Astrophysics, 

Student of Christ Church 

Chair Gemini Telescopes Board 

PPARC Senior Research Fellow 


• Galaxy Mapping with the SAURON Integral Field Spectrograph: the Star Formation 

History of NGC 4365, Davies, R. L., et al., 2001, Astrophys. J. Letters 548, L33. 

• Early-type galaxies in low-density environments, Harald Kuntschner, + Roger L. 

Davies, 2002, Mon. Not. Roy. Astr. Soc. 337, 172. 

• A SAURON study of M32: measuring the intrinsic flattening and the central black 

hole mass, Verolme, E. K., + Davies, R. L. et al 2002, Mon. Not. Roy. Astr. Soc., 

335, 517. 

• Galaxy Properties in Low X-Ray Luminosity Clusters at z=0.25, Michael L. Balogh, 

+ R.L. Davies, et al 2002, Mon. Not. Roy. Astr. Soc. 337, 256 

• Gemini-north multiobject spectrograph optical performance, Murowinski R., + 

Davies R. L. et al 2003, in Proc of SPIE 4841, 1440-1451.



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Eric Emsellem Science team Lyon 

Astronomer 

Member of the Space Telescope Users Committee 

Member of the Scientific Advisory Council of CFHT 

Chair of the french Time Allocation Committee of CFHT 

Member of the Scientific Committee of the french National Program for Galaxies 

CoI of SAURON instegral field spectrograph 

Member of the scientific committee of SINFONI/VLT 


• The SAURON project – III. Integral field absorption line kinematics of 48 elliptical 

and lenticular galaxies, Emsellem, E., Cappellari, M., Peletier, R., et al., MNRAS, 

submitted 

• Difficulty with Recovering The Masses of Supermassive Black Holes from Stellar 

Kinematical Data, Valuri, M., Merritt, D., Emsellem, E., ApJ, in press 

• A two-arm gaseous spiral in the inner 200 pc of the early-type galaxy NGC 2974: 

signature of an inner bar, Emsellem, E., Goudfrooij, P., Ferruit, P., MNRAS, 345, 

1297 

• Galaxies: The Third Dimension - Conference Summary, Emsellem, E., Bland- 

Hawthorn, J., 2002, ASP Conference Proceedings, Vol. 282. Edited by Margarita 

Rosado, Luc Binette, and Lorena Arias. ISBN: 1-58381-125-7. San Francisco: 

Astronomical Society of the Pacific, 2002., p.539 

• The SAURON project - II. Sample and early results, de Zeeuw, T., Bureau, M., 

Emsellem, E., et al., 2002, MNRAS, 329, 513 

Pierre Ferruit Science team Lyon 

Adjunct Astronomer at CRAL 

Euro3D CRAL WP leader 

CoI of NIRSPEC/JWST phase A study 


• A two-arm gaseous spiral in the inner 200 pc of the early-type galaxy NGC 2974: 

signature of an inner bar, Emsellem, Goudfrooij & Ferruit, 2003, MNRAS,345,1297 

• Spatial Resolution of High-Velocity Filaments in the Narrow-Line Region of NGC 

1068: Associated Absorbers Caught in Emission?, Cecil, Dopita, 

Groves, Wilson, Ferruit, Pécontal, Binette, 2002,ApJ, 568, 627 

• Chandra X-Ray Observations of NGC 4151, Yang, Wilson, Ferruit, 2001, ApJ, 563, 

124 

• Nuclear Gasdynamics in Arp 220: Subkiloparsec-Scale Atomic Hydrogen Disks, 

Mundell, Ferruit, Pedlar, 2001, ApJ, 560,168



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Olivier Le Fevre Science team Marseille 

Astronomer 

Director, Laboratoire d'Astrophysique de Marseille. 

Observational cosmology, large deep surveys 

PI, VIMOS VLT Deep Survey 

Instrumentation development (CFHT-MOS-SIS, VLT-VIMOS, JWST-NIRSPEC) 


• The Canada-France Redshift Survey VIII: evolution of the clustering of galaxies from 

z~1, Le Fevre, O., Hudon, D., Lilly, S.J., Crampton, D., Hammer, F., Tresse, L., 

1996, Ap.J., 461, 534. 

• The Canada-France Redshift Survey XIII: the luminosity density and star-formation 

history of the universe to z~1, Lilly, S.J., Le Fevre, O., Hammer, F., Crampton, D., 

Ap.J., 1996, 460, L1. 

• HST imaging of a sample of CFRS and LDSS galaxies IV. the influence of mergers 

in the evolution of field galaxies, Le Fevre, Abraham, Ellis, Lilly, et al., 2000, 

MNRAS, 311, 565 

• The VIRMOS deep imaging survey: I. overview and survey strategy O. Le Fevre, Y. 

Mellier, et al., A&A, in press (astro-ph/0306252) 

• Discovery of a z = 6.17 galaxy from CFHT and VLT observations, J.-G. Cuby, O. Le 

Fevre, H. McCracken, J.-C. Cuillandre, E. Magnier, B. Meneux, Astron.Astrophys. 

405 (2003) L19 

Marijn Franx Science team Leiden 

Professor of Extra-Galactic Astronomy, University of Leiden 

Member ACS Science Team 

PI, "Faint Infra-Red Extragalactic Survey" 


• The Rest-Frame Optical Luminosity Density, Color, and Stellar Mass Density of the 

Universe from z = 0 to z = 3, Rudnick, G. + Franx, M. et al., 2003, ApJ, 599, 847 

• Star Formation at z~6: i-Dropouts in the Advanced Camera for Surveys Guaranteed 

Time Observation Fields, Bouwens, R. J. + Franx, M. et al, 2003, ApJ 595, 589 

• Large Disklike Galaxies at High Redshift, Labbe, I., + Franx, M. et al., 2003, ApJL, 

591, L95 

• Spectroscopic Confirmation of a Substantial Population of Luminous Red Galaxies at 

Redshifts z>2, van Dokkum, P., G., + Franx M. et al., 2003, ApJL, 587, L83 

• A Significant Population of Red, Near-Infrared-selected High-Redshift Galaxies, 

Franx, M., et al., 2003, ApJ, 587, L79



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Gerard Gilmore Science team Cambridge 

Professor of Experimental Philosphy, Cambridge University 

Deputy Director, Institute of Astronomy, Cambridge 

The Royal Society Smithson Fellow, King's College, Cambridge 

Chair, EU Optical Infrared Coordination Committee for Astronomy 

Chair, UK PPARC ESO and Gemini Scientific Advisory Committee 

Editor, New Astronomy; Editorial Board, NA, NA reviews, ASP. 


• First Clear Signature of an Extended Dark Matter Halo in the Draco Dwarf Spheroidal 

(Kleyna + Gilmore etal) ApJL 563 L115 2001 

• GAIA: Composition, formation and evolution of the Galaxy (Perryman + Gimore et 

al ) A+A 369 339 2001 

• Non-parametric star formation histories for four dwarf spheroidal galaxies of the 

Local Group (Hernandez + Gilmore et al) MNRAS 317 831 2000 

• The White Dwarf Cooling Age of the Open Cluster NGC 2420 (von Hippel + 

Gilmore et al) AJ 120 1384 2000. 

Bruno Guiderdoni CoI Lyon 

Directeur de recherché au CNRS 

Head of the group cosmological simulation at IAP 

Associate scientist on HERSHEL and PLANCK 


• G. Kauffmann, S. White & B. Guiderdoni, “The formation and evolution of galaxies 

within merging dark matter haloes”, 1993, MNRAS, 264, 201 

• J. Devriendt, B. Guiderdoni & R. Sadat, “Galaxy modelling - I. Spectral Energy 

Distributions from far--UV to submm wavelengths”, 1999, A&A, 350, 381 

• R. Sadat, B. Guiderdoni & J. Silk, “Cosmological history of stars and metals”, 2001, 

{\it Astron. Astrophys.}, {\bf 369}, 26 

• S. Hatton, J. Devriendt, S. Ninin, F.R. Bouchet, B. Guiderdoni, & D. Vibert, “GalICS 

I: A hybrid N-body/semi--analytic model of hierarchical galaxy formation”, 2002, 

MNRAS,



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Simon Lilly CoI Zurich 

Professor ETH Zurich (Chair of Experimental Astrophysics) 

President, IAU Commission #47 "Cosmology" 

Interdisciplinary Scientist, JWST Flight Science Working Group 

Member, JWST NIRCam Flight Science Team 

SANW representative, Opticon 

Member, ESO Science and Technical Committee 


• Lilly, S.J., Le Fèvre, O., Hammer, F., Crampton D., 1996, "CFRS XIII: The 

luminosity density and star-formation rate of the Universe back to z ~ 1", 

Astrophys.J.Lett., 460, L1. 

• Lilly, S.J., Schade, D.J., Ellis, R.S., Le Fèvre, O., Brinchmann, J., , Tresse, L., 

Abraham, R., Hammer, F., Crampton, D., Colless, M.M., Glazebrook, K., Mallen- 

Ornelas, G., Broadhurst, T.J., 1998, “Hubble Space Telescope imaging of the CFRS 

and LDSS redshift surveys II: Structural parameters and the evolution of disk galaxies 

to z ~ 1”, Astrophys.J., 500, 75. 

• Lilly, S.J., Eales, S.A.; Gear, W.K.; Hammer, F.; Le Fèvre, O.; Crampton, D.; Bond, 

J. R.; Dunne, L., “The Canada-United Kingdom Deep Submillimeter Survey. II. First 

Identifications, Redshifts, and Implications for Galaxy Evolution”, 1999, Astrophys.J., 

5 

• Crampton, D., Schade, David, Hammer, F., Matzkin, A., Lilly, S. J., Le Fèvre, O.; 

2002, “The Gravitational Lens CFRS 03.1077”, ApJ, 570, 86. 

• Webb, T. M.; Eales, S.; Foucaud, S.; Lilly, S. J.; McCracken, H.; Adelberger, K.; 

Steidel, C.; Shapley, A.; Clements, D. L.; Dunne, L.; 2003, “The Canada-United 

Kingdom Deep Submillimeter Survey. V. The Submillimeter Properties of Lyman 

Break Galaxies”, ApJ 582, 6. 

Richard McDermid Deputy Instrument Scientist Leiden 

Postdoctoral Researcher 

CoI of SAURON Project 

Member of WHT OASIS/NAOMI science team 


• E. Emsellem, M. Cappellari, R. Peletier, R. McDermid, et al."The SAURON III: 

Absorption line kinematics of 48 E/SOs", 2004 MNRAS submitted 

• R. McDermid, H. Kuntschner, R. Davies & A. Vazdekis,"Young Disks in Elliptical 

Galaxies?", 2004 MNRAS submitted 

• R. McDermid, et al. "OASIS high-resolution observations of the SAURON ellipticals 

and lenticulars", Euro3D Conf. Proc.: Astron. Nach. 2004, 325, 2



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Page: 21/25 

Simon L. Morris Science team Durham 

Reader, Physics Department, University of Durham 

UK Astronomy Advisory Panel 

Project Scientist for the Gemini Adaptive Optics System (Altair) 1997-2000 


• J. B. Hutchings, S. L. Morris and D. Crampton, 2001, AJ, 121, 80, “Emission-Line 

Imaging of QSOs with High Resolution” 

• R. G. Carlberg, H. K. C. Yee, S. L. Morris, H. Lin, P. B. Hall, D. R. Patton, M. 

Sawicki, and C. W. Shepherd, 2001, ApJ, 552, 427, “Galaxy Groups at Intermediate 

Redshift” 

• C. W. Shepherd, R. G. Carlberg, H. K. C. Yee, S. L. Morris, H. Lin, M. Sawicki, P. 

B. Hall and D. R. Patton, 2001, ApJ, 560, 72, “The Galaxy correlation function in the 

CNOC2 Redshift survey: Dependence on color, luminosity and redshift” 

• R. G. Carlberg, H. K. C. Yee, S. L. Morris, H. Lin, P. B. Hall, D. R. Patton, M. 

Sawicki, and C. W. Shepherd, 2001, ApJ, 563, 736, “Environment and Galaxy 

Evolution at Intermediate Redshift in the CNOC2 Survey” 

Emmanuel Pécontal AIT instrument scientist Lyon 

Assistant Astronomer, Centre de Recherche Astronomique de Lyon 

Instrument scientist of the Supernovae Integral Field Spectrograph (Part of the SNfactory 

international project) 

Member of the French Supernovae Consortium Board 


• Overview of the Nearby Supernova Factory, Aldering + Pécontal et al, SPIE 4836, 

61A 2002 

• Spatial Resolution of High-Velocity Filaments in the Narrow-Line Region of NGC 

1068: Associated Absorbers Caught in Emission? Cecil + Pécontal et al, ApJ 568 627 

2002 

• Dynamics of embedded bars and the connection with AGN. I. ISAAC/VLT 

stellar kinematics, Emsellem + Pécontal et al, A&A 368 52 2001 

• Integral field spectroscopy of the radio galaxy 3C 171, Marquez + Pécontal et al, 

A&A 361 5 2000 

• The extended emission-line region of the Seyfert galaxy Mrk 573, Ferruit + Pécontal 

et al, MNRAS 309 1 1999



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Patrick Pinet Science team Toulouse 

Directeur de Recherche au CNRS 

Member, French National Program of Planetology Committee 

Member, Solar System Group, French Space Agency (CNES) 

Co-I on the Japanese (JAXA) Lunar-A and Selene missions (spectro/imaging instruments) 

Co-I on the European (ESA) Mars Express mission (HRSC and OMEGA instruments) 

Co-I on the European (ESA) Smart-1 mission (AMIE instrument) 


• Pinet, P.C., V.V. Shevchenko, S.D. Chevrel, Y.H. Daydou, C. Rosemberg, Local and 

regional lunar regolith characteristics at Reiner Gamma Formation: Optical and 

spectroscopic properties from Clementine and earth-based data: J. Geophys;Res., 105, 

E4, 9457-9475, 2000. 

• Chevrel, S.D., P.C. Pinet, Y. Daydou, S. Maurice, W.C. Feldman, D.J. Lawrence, 

P.G. Lucey, Integration Of The Clementine Uv-Vis Spectral Data And The Lunar 

Prospector Gamma-Ray Data: A Global Scale Multielement Analysis Of The Lunar 

Surface Using Iron, Titanium And Thorium Abundances, J.G.R. Planets, 107, E12. 

• Cord, A., P.C. Pinet, Y. Daydou, And S. Chevrel, Planetary Regolith Surface Analogs 

Optimized Determination. Of Hapke Parameters Using Multi-Angular Spectro- 

Imaging Laboratory Facility, Icarus, Vol. 165, Issue 2 , 414-427, 2003 

• Shkuratov, Y., D. Stankevitch, V. Kaydash, V. Omelchenko, C. Pieters, P.C. Pinet, S. 

Chevrel, Y. Daydou, B. Foing, Z. Sodnik, L. Taylor, V.V. Shevchenko, Composition 

of The Lunar Surface As Will Be Seen From Smart-1 : A Simulation Using 

Clementine Data, J. Geophys. Res. Planets, 108, E4, Doi:10.1029/2002je001971, 

2003. 

Andreas Quirrenbach AO instrument scientist Leiden 


Martin Roth Software instrument scientist Potsdam 

Team leader, Astrophysikalisches Institut Potsdam 

PI of PMAS Integral Field Spectrograph 

PI of ULTROS Project (Ultra-deep Optical 3D spectroscopy) 

Coordinator Euro3D Research Training Network (EU) 


• 3D spectrophotometry of Planetary Nebulae in the Bulge of M31, Roth M. et al, 2004, 

ApJ in press 

• Planetary nebulae and HII regions in NGC 300, Soffner + Roth M et al, 1996, A&A, 

306, 9 

• Deep optical spectroscopy with PMAS: using the no-and-shuffle technique, Roth M. 

et al, 2004, Exp. Astron., in press 

• PMAS design and integration, Roth M. et al, 2000, SPIE 4008, 277



Issue: 1.0 

Date: 02/02/04 

Page: 23/25 

Sebastian Sanchez Science team Potsdam 

Euro3D RTN Postdoc, Astrophysikalisches Institut Potsdam. 

Member of the GEMS collaboration. 


• The Merger/AGN connection: A case for 3D spectroscopy, S.F.Sanchez, 

L.Christensen, T.Becker, et al., 2004, AN, 325, 112 

• E3D, The Euro3D visualization tool I: Description of the program and its capabilities", 

S.F.Sanchez, 2004, AN, 325, 167 

• Integral field spectroscopy of extended Ly α emission from the DLA galaxy in 

Q2233+131", L.Christensen, S.F.Sanchez, T.Becker, et al., 2003, AA, accepted 

(astro-ph/0401051) 

• The host galaxies of the AGNs from GEMS at 0.5



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Date: 02/02/04 

Page: 24/25 

Niranjan Thatte Instrument scientist Oxford 

Lecturer in Astrophysics, University of Oxford 

Team leader (PI) for the SWIFT integral field spectrograph 

Member of NIRSpec/JWST Science Study Team of ESA 

PI of SPIFFI – a cryogenic near-IR integral field spectrograph for the VLT 

Co-I of LUCIFER – a multi object spectrograph for the LBT 

Primary responsible for the MPE 3D near-IR integral field spectrograph 


• Eisenhauer, F.,Tecza, M., Thatte, N., Genzel, R., Abuter, R., Iserlohe, C., 

Schreiber, J., Huber, S., Roehrle, C., Horrobin, M., and 22 coauthors, The Universe in 

3D: First Observations with SPIFFI, the Infrared Integral Field Spectrometer for the 

VLT, ESO Messenger, 2003, 113, 17. 

• Mengel, S., Lehnert, M. D., Thatte, N., Genzel, R., Dynamical masses of young star 

clusters in NGC 4038/4039, 2002, A&A, 383, 137. 

• Davies, R. I., Tecza, M.,Looney, L. W., Eisenhauer, F., Tacconi- 

Garman, L. E.,Thatte, N., Ott, T., Rabien, S., Hippler, S., Kasper, M., Adaptive 

Optics Integral Field Spectroscopy of the Young Stellar Objects in LKHα 225, 2001, 

ApJ, 552, 692 

• Thatte, N., Tecza, M., Genzel, R., Stellar dynamics observations of a double nucleus 

in M 83, 2000, A&A, 364, L47. 

Lutz Wisotzki Science team Potsdam 

Senior astronomer at the Astrophysical Institute Potsdam 

Head of the Galaxies Division at AIP 

Lecturer at Potsdam University 


• The Hamburg/ESO survey for bright QSOs. III. A large flux-limited sample of QSOs', 

Wisotzki L., Christlieb N., Bade N., Beckmann V., Köhler T., Vanelle C., Reimers 

D., 2000, A&A 358, 77 

• The evolution of faint AGN between z ~ 1 and z ~ 5 from the COMBO-17 survey, 

Wolf C., Wisotzki L., Borch A., Dye S., Kleinheinrich M., Meisenheimer K., 2003, 

A\&A, 408, 499 

• Integral field spectophotometry of the quadruple QSO HE 0435-1223: Evidence for 

microlensing, Wisotzki L., Becker T., Christensen L., Helms A., Jahnke K., Kelz A., 

Roth M.M., Sanchez S.F., 2003, A&A, 408, 455, 

• Integral Field Spectroscopy of Extended Lyman-alpha Emission from the DLA 

Galaxy in Q2233+131, Christensen L., Sanchez S.F., Jahnke K., Becker T., Wisotzki 

L., Kelz A., Popovic L.C., Roth, M.M., 2004, A&A in press, astro-ph/0401051



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Date: 02/02/04 

Page: 25/25 

Tim de Zeeuw CoI Leiden 

Director, Netherlands Research School for Astronomy NOVA 

Director, Leiden Observatory 

Member, ESO Council 

Chair, Space Telescope Institute Council 

Member, AURA Board of Directors 

co-PI, SAURON Integral-field spectrograph 

Member, ESO/MPE SINFONI Science Team 


• Evidence for Massive Black Holes in Nearby Galactic Nuclei, de Zeeuw P.T., 2001. 

In ESO Conference on Black Holes in Binaries and Galactic Nuclei, eds L. Kaper, 

E.P.J. van den Heuvel, 78-87 

• The SAURON Project. II. Sample and early results, de Zeeuw P.T., Bureau M., 

Emsellem E., Bacon R., Carollo C.M., Copin Y., Davies R.L., Kuntschner H., Miller 

B.W., Monnet G., Peletier R.F., Verolme E.K., 2002. MNRAS, 329, 513-530 

• A SAURON Study of M32: measuring the intrinsic flattening and the central black 

hole mass, Verolme, E.K., Cappellari, M., Copin Y., van der Marel R.P., Bacon R., 

Bureau M., Davies R.L., Miller B.M., de Zeeuw P.T., 2002. MNRAS, 335, 517-525 

• Conference Summary: Co-evolution of Black Holes and Galaxies de Zeeuw P.T., 

2004, in Carnegie Centennial Symposium I. Co-evolution of Black Holes and 

Galaxies, ed. L. Ho, Cambridge University Press, in press (astro-ph/0303469).

MUSE 

Data Analysis 

Software Tools 

Written by : Eric Emsellem 



Issue : 2.2 

Date : 29/01/04 

File : 

muse_soft_dast.doc 

Distribution : Consortium 

History: 

• 20/04/03 – 1.0 First Issue 

• 30/12/03 – 2.0 Revised version with reshuffling 

• 20/01/04 – 2.1 Slightly revised version 

• 29/01/04 – 2.2 Final revision with A. Rousset


Issue: 2.2 

Date: 29/01/04 

Page: 2/8 


This document describes the data analysis software tools (DAST) of the Multi Unit 

Spectroscopic Explorer (MUSE) instrument. It is intended as a specification document for 

the: 

• General specifications and goals relevant to the DAST 

• Estimation of the manpower needed for the DAST 

1 Documents 

1.1 Applicable documents 

AD1 VLT Observatory Requirements for 

Scientific Instruments (+ AD12) 

VLT-SPE-10000-2723 

1.2 Reference documents 

RD1 MUSE, a wide-field 3D optical 

MUSE pre-phase A, final 

spectrometer, in response to ESO call for 

first proposal for 2 nd generation VLT 

instrument. 

RD2 MUSE Top Instrumental parameters MUSE-MEM-SCI-016 

RD3 

IDS … 

2 Acronyms 

AD 

ESO 

FoV 

MUSE 

NA 

PSF 

TBC 

TBD 

VLT 

DRS 

WP 

DAST 



Field of View 





To Be Defined 


Data Reduction Software 

Work Package 

Data Analysis Software Tools


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Date: 29/01/04 

Page: 3/8 

3 Scope 

The MUSE instrument will provide 90000 spectra of 3200 spectral pixels each per exposure covering a large 

part of the visible and near-infrared domain up to 1 micron. The Data Reduction Software will deliver fully 

extracted and calibrated datasets, i.e., with all the instrument signatures removed. However, the consortium 

is conscious that the full success of MUSE is not constrained by the success of the sole DRS (even if it is 

clearly the critical software component), and is therefore strongly motivated to allow an optimal scientific 

exploitation of the MUSE observations. The overall complexity of integral-field spectrographic data and the 

size of individual datacubes will somewhat prevent the user to have a direct and easy handle of the scientific 

content of the datasets. Any user could obviously apply his/her favorite tools to analyze MUSE datacubes. 

The consortium will however be in the best position to develop simple but robust dedicated tools to help the 

user in extracting high quality information from their data. It is also an important issue in the context of real 

time data reduction and analysis. The goal of the Data Analysis Software Tools Work Package is therefore to 

deliver, along with the instrument and Data Reduction Software, basic software tools which can produce 

scientific exploitable output from the available datacubes. This Work Package is obviously not defined as a 

MUSE deliverable, and will be conducted on a “best effort” basis. It is however the wish of the consortium 

to deliver most of the results from the DAST WP to the user community, as to optimize the scientific output 

of MUSE. We describe below the main components of such a DAST WP, starting with a reminder of our 

participation to the Euro3D Research Training Network. 

4 The Euro3D network: a common basis for software 

standards 

All MUSE consortium partners are currently engaged as participants of the Euro3D Research Training 

Network, one of whose goals is to develop these tools and a standard IFS data format, which is intended to 

be upward compatible. Quite a significant effort is on-going with the Euro3D Network, designed to diffuse 

3D Spectroscopy within the European (and presumably world) astronomical community. This includes 

providing state-of-the-art tools, and is now based on the Euro3D data FITS format. 

It is first critical to emphasize again that, although the DAST WP is not intended as deliverable of the 

MUSE project, it will be conducted in full compatibility with the other MUSE software components, so as 

to guarantee an easy and consistent implementation, particularly in the context of the constraints set by ESO. 

Considering the specific tasks conducted within the Euro3D Network: 

• The MUSE consortium acknowledges this effort, and will therefore develop the MUSE Analysis 

Tools in close contact with the Euro3D network. 

• Although MUSE is certainly a project on its own, the efforts of the institute/people involved in the 

software development should also profit to a wider community, to maximize the visibility and 

scientific output of the project, but also to maximize the visibility of the people themselves. 

The natural requirements would then be: 

To adopt the Euro3D FITS format. 

To guarantee tight communication links with the Euro3D network. 

Obviously, the final choice will depend on the constraints set by ESO in the context of the MUSE 

deliverables, and again this would natural leads to the adoption of rules common with the Data Reduction 

Software (DRS). Homogeneity and flexibility will be critical issues in the development of the DAST.


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In the following we describe in more details the components foreseen in the context of the DAST, which 

include pieces of software which should be considered as extension to MUSE deliverables within the DRS, 

as well as analysis tools more closely attached to specific science cases. 

5 Software extension to the DRS 

5.1 Mosaicing 

The DRS already includes the gathering of the 24 individual MUSE CCD components into one with the 

correction for the effect of differential refraction (when relevant). There is however a number of tasks which 

will be required to homogenise and merge individual MUSE fully calibrated datacubes. The goal here is to 

provide a rather general tool to allow optimal and flexible merging/mosaicing of individual MUSE 

exposures. 

This should include: 

1. Automated tool for recovering the centre (with respect to a fixed reference) of each individual 

exposure. This could make use of correlation techniques, and should very probably be guided by 

existing direct wide field images of the field. This could/should be linked with item 2 below. 

2. Relative or absolute Point Spread Function (PSF) measurements and renormalization (if required). 

The retrieval of the PSF on each individual exposures could, as in item 1, be guided by a higher 

resolution direct image of the field. This technique has been successfully used by the Lyon team in 

the past, but should be refined / generalized. Taking into account a probable variation of the PSF 

with wavelength is a requirement, particularly in the context of adaptive optics assisted 

spectrography. 

3. Closely linked to items 1 and 2, renormalization (e.g. transparency variations) of the images. It is 

highly probably that items 1, 2 and 3 will correspond to a single sub package/program since it 

makes a lot of sense to determine simultaneously the PSF, centre and normalization factor for a set 

of individual exposures. 

4. Drizzling of dithered cubes: an example of this exists in e.g. the treatment of HST exposures. 

Images are obtained with sub-pixels shifts and a super-resolved image is then derived by 

redistributing the flux in sub-pixels. A difficulty in the context of MUSE comes from the variation 

of (mainly) the PSF between individual exposures and with wavelength, but this could be (partially) 

solved by the PSF renormalization as mentioned in item 2. This item should also be closely linked 

with the DRS sky subtraction procedure. 

5. Treatment of residual cosmics, defective pixels, etc. This should take advantage of the mosaicing to 

compare different exposures when relevant, to remove e.g. residual cosmic rays impacts. 

6. Merging itself of individual exposures of the same field on some ''to be decided by the user'' grid. 

This should include the possibility to have different geometries (rectangular, optimal), and include 

an option to have a flux per surface or not (lens size dependent). 

7. Large mosaicing of exposures of different (adjacent or not) fields. This should include 

renormalization as described in items 1, 2 and 3, but could also include a tool for accurate 

astrometry.


Issue: 2.2 

Date: 29/01/04 

Page: 5/8 

5.2 Deconvolution 

Considering the impact of a varying PSF over the field (or over the spectral range), PSF renormalization 

may be quite a useful tool, as emphasized above. We can also foresee the usefulness of a deconvolution tool, 

which attempts to increase the spatial resolution of the obtained MUSE exposures, treating the full datacube 

in a consistent way. With the sampling provided by the High Resolution mode of MUSE, this can lead to 

very significant improvements in the detection of certain features, and could be critical for some scientific 

programs where high frequency structures are expected (e.g., probing central regions of galaxies). 

This tool could make use of some “reference” data to guide the deconvolution. A simple version of such a 

tool has already been implemented in the context of OASIS/CFHT observations (see Figure below), where 

high resolution HST images are used as constraints to a Lucy-Richardson deconvolution of the obtained 

datacubes. Such a tool is also included as a WP of the Euro3D Network, and specific development could 

take place in the specific context of instruments using MCAO such as MUSE. 

Figure 1 – 3D Deconvolution of a datacube: the central region of NGC 2974. Left panel: reconstructed 

emission line image before the deconvolution. Middle panels: reconstructed image and velocity field after 

the deconvolution. Right panel: HST narrow band image for comparison. From Emsellem, Goudfrooij, 

Ferruit, 2003, MNRAS, 345, 1297 

5.3 Binning and filtering 

Adaptive binning of datacubes (spectrally and/or spatially) is required to optimize the detection and/or 

measurement of specific features. The algorithm should be general enough as to allow flexible constraints 

set by the user. This could include constraints regarding e.g., the signal to noise ratio (spatial and/or spectral 

pixels), or the (spatial and/or spectral) frequency of the signal. 

Applications of such routines are numerous and we only provide here a few examples: 

• A mean PSF could then be optimally estimated by summing a number of point-like sources 

in a MUSE field. 

• Spatial binning of MUSE datacubes to ensure that the spatial signal to noise ratio is above a 

certain fixed minimum at every spatial bin.


Issue: 2.2 

Date: 29/01/04 

Page: 6/8 

• Spectral binning to ensure that the signal to noise ratio is above a certain fixed minimum at 

every spectral bin. 

• Fourier filtering of MUSE datacubes to ensure that only (spatial/spectral) structures which 

are sufficiently sampled remain. 

An example of such an algorithm has been recently developed e.g., for adaptive spatial binning of integralfield 

spectroscopic data under the constraint of keeping the signal to noise constant (centroidal Voronoi 

tesselation, see Cappellari & Copin 2003, MNRAS, 342, 345). 

Figure 2 - Example of the centroidal Voronoi tesselation (spatial adaptive binning) algorithm 

applied to SAURON integral field data. The right panel shows the signal to noise per spatial 

element before (crosses) and after (squares) the binning, and the left panel shows the shapes of the 

resulting bins and their centre (dots). From Cappellari & Copin 2003, MNRAS, 342, 345. 

6 Scientific Fields of application 

In this Section, we briefly describe the different tasks envisioned in the DAST WP, each one being linked to 

a specific scientific field of application. These links are however flexible, and the DAST items mentioned 

below should be viewed as general tools for the extraction of scientific information from the MUSE 

datacubes. 

It is important to emphasize that the development of such tools will obviously include extensive simulations 

and tests, making use of real and fake data. This will allow the creation of new routines and algorithms, 

which should significantly impact on the observational strategy applied for MUSE programs. This feedback 

on the design of scientific program is critical as to optimize the telescope time devoted to MUSE projects. 

It is also worth noting that many of the DAST routines will be required to closely communicate with a 

flexible database, which will contain e.g. some characteristics of the exposures, but will also gather the 

detected features. 

6.1 Deep Field – faint sources 

The goal is to provide tools for the analysis of datacubes obtained in the context of the MUSE deep field(s), 

as well as to allow the optimal detection / recovering of faint sources. The foreseen number of individual 

scientific exposures for a single deep field is about 80 (1 hour each). The recentring, renormalization, 

merging of these exposures is critical to optimize the quality of the output datacube, and therefore of the 

scientific results. As emphasized above, such a tool is a critical component of this project, and will certainly 

affect the way the observations are conducted (e.g. dithering). 

Note that the output of such a sub-package should communicate with the MUSE Database.


Issue: 2.2 

Date: 29/01/04 

Page: 7/8 

a) Optimal detection / recovering of structures: spectral and / or spatial. Since this is a critical task, at 

least two different approaches will be implemented here which will certainly provide different 

outputs, e.g. wavelet techniques, clustering.... 

b) Optimal detection of time varying features. This tool should obviously be closely linked with the 

one mentioned in item a). 

c) Quick calculation of ''simple'' quantities such as flux, redshift... There should be there a link with the 

line fitting tool, with some specific caution regarding the treatment of noise and the level of 

significance for detection purposes. 

6.2 Nearby galaxies – detailed studies 

The goal is to gather and adapt existing tools to allow quick and simple analysis of the datacubes pertaining 

to the detailed studies of galaxies: kinematics of stars and gas, disentangling stellar and gaseous luminosity 

contributions, line fitting, optimal fitting (e.g. template), stellar population tools. 

Note that many of the tools will be addressed with the Euro3D network, so that we must coordinate our 

efforts there. 

4 main streams can be followed here: 

1) Optimal fitting tools: allowing the disentangling of the stellar and gas contributions using either 

observed or theoretical stellar libraries (and extra components: e.g. non-thermal). 

2) Kinematics and deconvolution tools: cross-correlation, Fourier based or pixel fitting techniques 

3) Emission line fitting including complex line profiles and simultaneous fits to different systems of lines. 

The user should be able to easily set up complex linear or non-linear constraints on the unknown 

variables. 

4) Absorption line indices (for stellar population studies) 

6.3 Galactic Sources 

The goal here would be to provide tools to analyse galactic sources. This is rather meaningless at the 

moment since it may include very different processes/structures, extended or not. This could result in 

different sub packages or not, and should certainly be linked with Sub WP III. 

6.4 Stellar / Crowded Fields 

The goal is to provide analysis tools to optimally extract individual sources in (e.g., stellar) crowded fields, 

but is certainly meant to become more general that this. It should thus include tools which can e.g. 

disentangle the spectra of two blended sources (using some a priori constraints plus knowledge of the 

exposure characteristics). Note that the output of such a sub-package should communicate with the MUSE 

Database. 

7 Estimation of the manpower 

The following table provides a first estimate of the manpower required to conduct the tasks 

described in this document. The work is spread over the consortium, each sub-task being 

assigned to a manager. However, some sub-task will be conducted via the efforts of 

coordinated efforts from several institutes (e.g. mosaicing). Eric Emsellem will serve as a 

global coordinator for the work package. The FTE in Phase B are intended as a design phase, 

and in phase C as the actual development, testing and documenting.


Issue: 2.2 

Date: 29/01/04 

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Task Manager FTE 

(Phase B) 

FTE 

(Phase C) 

Mosaicing 

Martin Roth (Potsdam) 

Eric Emsellem (CRAL) 

0.7 2 

Deconvolution Eric Thiebaut (CRAL) 0.4 1 

Binning/Filtering Michele Cappellari (Leiden) 0.3 0.8 

Deep Field Simon Morris (Durham) 0.5 1 

Optimal fitting Eric Emsellem (CRAL) 0.2 0.4 

Emission line fitting Pierre Ferruit (CRAL) 0.2 0.6 

Kinematics Richard McDermid (Leiden) 0.2 0.5 

Line indices Marcella Carollo (Zurich) 0.2 0.4 

Galactic sources Martin Roth (Potsdam) 0.5 1 

Crowded fields Martin Roth (Potsdam) 0.4 1 

Total 3.6 8.7

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