MILCA, a Modified Internal Linear Combination Algorithm to extract astrophysical emissions from multi-frequency sky maps

TL;DR

MILCA is an advanced algorithm that improves the extraction of astrophysical signals from multi-frequency sky maps by correcting noise bias, incorporating known spectra, and utilizing external templates, demonstrated on simulated Planck data.

Contribution

The paper introduces MILCA, a generalized ILC method that handles multiple known-spectrum components, corrects noise bias, and integrates external templates for enhanced astrophysical signal separation.

Findings

MILCA accurately reconstructs Galactic CO emission.

MILCA effectively estimates the thermal Sunyaev-Zeldovich effect.

MILCA has been successfully applied within the Planck collaboration.

Abstract

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.