Planck working group 2.1 diffuse component separation review Paris november 2005

outline ● the goals of this working group ● recent results and ongoing activities ● status of the work ● problems ● plans

presentations at this meeting ● FastICA: harmonic space (Maino et al.), polarization (Stivoli et al.), Planck systematics (Donzelli et al.), BEAST data (Donzelli et al.), WMAP data (Banday et al.) ● MEM review (Stolyarov et al.) ● MEM and non-Gaussianity (Barreiro et al.) ● Second order statistics, Planck and WMAP applications (Bedini, Bonaldi et al.) ● SMICA (Cardoso et al.) ● SMICA on real data (Cardoso et al.)

criticalities, early 2004 ● single unique package ● flexibility ● maps and TODs ● blind and non-blind technique merging ● polarization ● point sources ● testing with incoming data

fragmented component separation ● algorithms are experiencing an high level of specialization, exploiting different assumptions, working in different domains, facing different issues ● it's unlikely one of them may do the whole work ● the component separation in Planck, even the part aiming at the diffuse emission, might optimize the results by exploiting different algorithms for different task

examples ● CMB power spectrum cleaning ● CMB imaging in total intensity ● CMB imaging in polarization ● upper limits on the B modes ● Galactic foreground recovery, total intensity and polarization ● solar system light ●...

setting the deliverables ● identifying a set of goals and deliverables that diffuse component separation algorithms can do on Planck data ● fixing the baseline deliverables, in connection with the DPCs level 3 duties ● orienting the algorithm development and design toward those goals ● performing tests in an enviroment which is as close as possible to the Planck instrument ● delivering the coded algorithms to the DPCs

baseline algorithms for Planck diffuse component separation ● the Planck level 3 DPC activity must deliver component maps, in total intensity and polarization, of the CMB plus the most important diffuse foregrounds ● we need to deliver the algorithms to do that, tested in an enviroment which is as close as possible to the actual experimental, classifying the effort in CMB cleaning, T and Q,U, foreground recovery, T and Q,U

extras ● CMB imaging preserving the underlying CMB statistics ● polarization component separation in the foreground cleanest regions in order to get upper limits on B modes ●...

works since the last report ● Stolyarov et al. (astro-ph/ ) extended the flexible Maximum Entropy Method (MEM) in order to be effective when the noise is not distributed uniformly in the sky, as well as for more realistic foregrounds, for example including in the thermal dust emission. ● Naselsky et al. (astro-ph/ ) performed a phase analysis comparing Wilkinson Microwave Anisotropy Probe (WMAP) data at different frequencies, in order to isolate the components exhibiting a marked spatial variation of the frequency spectral index.

works since the last report ● Moudden et al. (astro-ph/ ) extended the Spectral Matching ICA (SMICA) to include the use of wavelets to deal with non-stationary data as well as gaps. ● Bedini et al. (astro-ph/ ) proposed a new approach exploiting the spatial correlations in the data as well as any existing prior on the signal to recover in order to estimate the matrix mixing the different emissions to be extracted from the data.

works since the last report ● Patanchon et al. (astro-ph/ ) applied an Independent Component Analysis (ICA) approach to the foreground cleaned CMB map constructed via internal linear combination by the data analysis team of the WMAP satellite. ● Barreiro et al. (astro-ph/050303) performed a study of the effect of the MEM and Wiener Filtering (WF) component separation techniques on the CMB statistics.

works since the last report ● Stivoli et al. (astro-ph/ ) presented a parallelized vertion of ICA (lightICA) working in total intensity as well as polarization and allowing to estimate errors in the CMB reconstruction by means of Monte Carlo (MC) chains. ● Naselsky et al. (astro-ph/ ) exploit specific measures of phase correlations exploiting geometrical features and symmetries of the Galactic plane in order to remove the Galactic components from simulated data.

works since the last report ● Donzelli et al. (astro-ph/ ) applied the FastICA algorithm to the data from the Beast experiment, in order to recover the CMB component. ● Eriksen et al. (astro-ph/ ) proposed a new approach to the component separation based on a parametric description of the different foreground models, and on extensive Monte Carlo Markov Chains (MCMC) techniques in order to track the foreground errors down to the cosmological parameter estimation.

status ● despite of the high level of interest, the work is not enough Planck oriented yet ● still different authors work independently, on independently simulated datasets ● no comparison between different codes

problems ● poor testing together with point source extraction with diffuse component separation ● testing with Planck systematics still missing ● poor effort for polarization ● poor effort toward integration with the DPCs pipeline ● WMAP diffuse component separation analysis by the Planck collaboration still missing

plans ● the official end-to-end Planck tests are beginning in a few months ● a sub-set of the reference sky is probably being prepared for those tests ● the proposal might be to perform component separation tests with the baseline algorithms on the maps which are output from those tests ● a reasonable number of frequencies and foregrounds should be implemented from the beginning in the simulations, to be able to apply the baseline algorithms

advantages ● the codes are finally tested together and on a common simulated dataset ● the codes are brought to the DPCs ● they are finally tested in a Planck enviroment, including increasingly nasty systematics ●... ● if we don't do that, the expectation is that it is done anyway, independently on the two DPCs, with very little role by this working group as a whole