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N. Mandolesi et al.: The Planck-LFI programmemap-making algorithm will be applied to produce a map fromeach receiver.The instrument model allows one to check and control systematiceffects and the quality of the removal performed bymap-making and calibration of the receiver map. Receiver mapscleaned of systematic effects at different levels of accuracywill be stored into a calibrated map archive. The productionof frequency-calibrated maps will be performed by processingtogether all receivers from a givenfrequencychannelinasinglemap-making run. In Figs. 13 and 14, wereportthestepsperformedby Level 2, together with the associated times foreseen.Fig. 12. Level 1 structure.6.2. DPC Level 2At this level, data processing steps requiring detailed instrumentknowledge (data reduction proper) will be performed. The rawtime series from Level 1 will also be used to reconstruct a numberof calibrated scans for each detector, as well as instrumentalperformance and properties, and maps of the sky for each channel.This processing is iterative, since simultaneous evaluationof quite a number of parameters should be made before the astrophysicalsignal can be isolated and averaged over all detectorsin each frequency channel. Continuous exchange of informationbetween the two DPCs will be necessary at Level 2 to identifyany suspect or unidentified behaviour or any results fromthe detectors.The first task that the Level 2 performs is the creation ofdifferenced data. Level 1 stores data from both Sky and Load.These two have to be properly combined to produce differenceddata, therefore reducing the impact of 1/ f noise achieved bycomputing the so-called gain modulation factor R, whichisderivedby taking the ratio of the mean signals from both Skyand Load.After differenced data are produced, the next step is the photometriccalibration that transforms the digital units into physicalunits. This operation is quite complex: different methods are implementedin the Level 2 pipeline that use the CMB dipole asan absolute calibrator allowing for the conversion into physicalunits.Another major task is beam reconstruction, which is implementedusing information from planet crossings. An algorithmwas developed that performs a bi-variate approximation of themain beam section of the antenna pattern and reconstructs theposition of the horn in the focal plane and its orientation withrespect to a reference axis.The step following the production of calibrated timelinesis the creation of calibrated frequency maps. To achieve this,pointing information will be encoded into time-ordered pixelsi.e., pixel numbers in the given pixelisation scheme (HEALPix)by identifying a given pointing direction that is ordered in time.To produce temperature maps, it is necessary to reconstruct thebeam pattern along the two polarisation directions for the main,intermediate, and far parts of the beam pattern. This will allowthe combination of the two orthogonal components intoa single temperature timeline. On this temperature timeline, a6.3. DPC Level 3The goal of the DPC Level 3 is to estimate and characterisemaps all the different astrophysical and cosmological sources ofemission (“components”) present at Planck wavelengths. Usingthe CMB component obtained after point-source extraction andcleaning from diffuse, Galactic emission, the APS of the CMBis estimated for temperature, polarisation, and cross temperature/polarisationmodes.The extraction of the signal from Galactic point-like objects,and other galaxies and clusters is achievedasafirststep,eitherusing pre-existing catalogues based on non-Planck data, or filteringthe multi-frequency maps with optimal filters to detect andidentify beam-like objects (see Herranz et al. 2009, andreferencestherein).The algorithms dedicated to the separation of diffuse emissionfall into four main categories, depending on the criteria exploitedto achieve separation, and making use of the wide frequencycoverage of Planck (see Leach et al. 2008, andreferencestherein). Internal linear combination and template fittingachieves linear mixing and combination of the multi-frequencydata with other data sets, optimized for CMB or foreground recovery.The independent component analysis works in the statisticaldomain, without using foreground modelling or spatialcorrelations in the data, but assuming instead statistical independencebetween the components that are to be recovered. Thecorrelated component analysis, on the other hand, makes use ofaparametrizationofforegroundunknowns,andusesspatialcorrelationsto achieve separation. Finally, parametric methods consistof modelling foreground and CMB components by treatingeach resolution element independently, achieving fitting of theunknowns and separation by means of a maximum likelihoodanalysis. The LFI DPC Level 3 includes algorithms that belongto each of the four categories outlined above. The complementarityof different methods for different purposes, as well as thecross-check on common products, are required to achieve reliableand complete scientific products.As for power spectrum estimation, two independentand complementary approaches have been implemented(see Gruppuso et al. 2009, andreferencestherein):aMonte-Carlo method suitable for high multipoles (based on theMASTER approach, but including cross-power spectra from independentreceivers); and a maximum likelihood method for lowmultipoles. A combination of the two methods will be used toproduce the final estimation of the APS from LFI data, beforeits combination with HFI data. In Fig. 15, wereportthestepsperformed in the Level 3 pipelinewiththeassociatedtimescalesforeseen.The inputs to the Level 3 pipeline are the three calibrated frequencymaps from LFI together with the six calibrated HFI frequencymaps that should be exchanged on a monthly basis. TheLevel 3 pipeline has links with most of the stages of the Level 1Page 17 of 24
A&A 520, A3 (2010)Fig. 13. Level 2 calibration pipeline.and Level 2 pipelines, and therefore the most complete anddetailed knowledge of the instrumental behaviour is importantfor achieving its goals. Systematic effects appearing in the timeordereddata, beam shapes, band width, source catalogues, noisedistribution, and statistics are examples of important inputs tothe Level 3 processing. Level 3 will produce source catalogues,component maps, and CMB power spectra that will be deliveredto the PLA, together with other information and data needed forthe public release of the Planck products.6.4. DPC Level SIt was widely agreed within both consortia that a software systemcapable of simulating the instrument footprint, starting fromapredefinedsky,wasindispensableforthefullperiodofthePlanck mission. Based on that idea, an additional processinglevel, Level S, was developed and upgraded whenever the knowledgeof the instrument improved (Reinecke et al. 2006). Level Snow incorporates all the instrument characteristics as they wereunderstood during the ground test campaign. Simulated datawere used to evaluate the performance of data-analysis algorithmsand software against the scientific requirements of themission and to demonstrate the capability of the DPCs to workusing blind simulations that contain unknown parameter valuesto be recovered by the data processing pipeline.6.5. DPC software infrastructureDuring the entire Planck project, it has been (and will continueto be) necessary to deal with aspects related to informationmanagement, which pertain to a variety of activitiesconcerning the whole project, ranging from instrumentinformation (e.g., technical characteristics, reports, configurationcontrol documents, drawings, public communications) tosoftware development/control (including the tracking of eachbit produced by each pipeline). For this purpose, an integrateddata and information system (IDIS) was developed. IDIS(Bennett et al. 2000) isacollectionofsoftwareinfrastructurePage 18 of 24
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A&A 520, E1 (2010)DOI: 10.1051/0004
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ABSTRACTThe European Space Agency
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A&A 520, A1 (2010)Fig. 2. An artist
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M. Sandri et al.: Planck pre-launch
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arrangement was also constrained by
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