MILCA, a Modified Internal Linear Combination Algorithm to extract
  astrophysical emissions from multi-frequency sky maps
  The analysis of current Cosmic Microwave Background (CMB) experiments is
based on the interpretation of multi-frequency 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
multi-frequency data. We present here a Modified Internal Linear Component
Algorithm (MILCA) that generalizes the ILC approach to the case of multiple
astrophysical components for which the electromagnetic spectrum is known. In
addition MILCA corrects for the intrinsic noise bias in the standard ILC
approach and extends it to an hybrid space-frequency representation of the
data. It also allows us to use external templates to minimize the contribution
of extra components but still using only a linear combination of the input
data. We apply MILCA to simulations of the Planck satellite data at the HFI
frequency bands. We explore the possibility of reconstructing the Galactic
molecular CO emission on the Planck maps as well as the thermal
Sunyaev-Zeldovich effect. We conclude that MILCA is able to accurately estimate
those emissions and it has been successfully used for this purpose within the
Planck collaboration.
