2013
DOI: 10.1051/0004-6361/201321891
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MILCA, a modified internal linear combination algorithm to extract astrophysical emissions from multifrequency sky maps

Abstract: This analysis of current cosmic microwave background (CMB) experiments is based on the interpretation of multifrequency sky maps in terms of different astrophysical components and it requires specifically tailored, component separation algorithms. In this context, internal linear combination (ILC) methods have been extensively used to extract the CMB emission from the WMAP multifrequency data. We present here a modified internal linear component algorithm (MILCA) that generalizes the ILC approach to the case o… Show more

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Cited by 152 publications
(159 citation statements)
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“…MILCA (Hurier et al 2010): The thermal SZ signal reconstruction is performed on the six Planck all-sky maps from 100 GHz to 857 GHz. MILCA (Modified Internal Linear Combination Algorithm) is a component separation approach aiming at extracting a chosen component (here the thermal SZ signal) from a multi-channel set of input maps.…”
Section: Appendix A: Sz Map Reconstruction Methodsmentioning
confidence: 99%
“…MILCA (Hurier et al 2010): The thermal SZ signal reconstruction is performed on the six Planck all-sky maps from 100 GHz to 857 GHz. MILCA (Modified Internal Linear Combination Algorithm) is a component separation approach aiming at extracting a chosen component (here the thermal SZ signal) from a multi-channel set of input maps.…”
Section: Appendix A: Sz Map Reconstruction Methodsmentioning
confidence: 99%
“…We selected a sample of cluster pairs whose difference in redshift is smaller than 0.01 and whose angular distance is between 10 and 120 . For all selected pairs we constructed maps of the tSZ (tSZ hereafter) emission from the Planck HFI frequency maps at a resolution of 7.18 using different component separation techniques: MILCA (maximum internal linear component analysis, Hurier et al 2010), NILC (needlet internal linear combination, Remazeilles et al 2011) and GMCA (generalized morphological component analysis, Bobin et al 2008). A detailed discussion of the relative performance of these component separation techniques can be found in Planck Collaboration (2013) and Melin et al (2012).…”
Section: Pairs Of Merging Clusters In Planckmentioning
confidence: 99%
“…The Comptonisation parameter y maps used in this work have been obtained using the MILCA (modified internal linear combination algorithm) method (Hurier et al 2010) on the Planck frequency maps from 100 GHz to 857 GHz in a region centred on the Coma cluster. MILCA is a component separation approach aimed at extracting a chosen component (in our case the thermal Sunyaev Zeldovich, tSZ, signal) from a multi-channel set of input maps.…”
Section: Reconstruction and Analysis Of The Y Mapmentioning
confidence: 99%