TL;DR
This paper demonstrates that the GMCA sparsity-based algorithm can effectively separate 21 cm cosmological signals from complex foregrounds, including polarization leakage, in full-sky intensity maps, with minimal bias.
Contribution
First application of GMCA to remove polarization leakage in 21 cm intensity mapping without prior assumptions, showing robust performance in realistic simulations.
Findings
GMCA recovers the angular power spectrum with ~5% bias for ll>25.
GMCA maintains performance with up to 40% missing channels.
Polarization leakage significantly impacts component separation results.
Abstract
21 cm intensity mapping has emerged as a promising technique to map the large-scale structure of the Universe. However, the presence of foregrounds with amplitudes orders of magnitude larger than the cosmological signal constitutes a critical challenge. Here, we test the sparsity-based algorithm Generalised Morphological Component Analysis (GMCA) as a blind component separation technique for this class of experiments. We test the GMCA performance against realistic full-sky mock temperature maps that include, besides astrophysical foregrounds, also a fraction of the polarized part of the signal leaked into the unpolarized one, a very troublesome foreground to subtract, usually referred to as polarization leakage. To our knowledge, this is the first time the removal of such component is performed with no prior assumption. We assess the success of the cleaning by comparing the true and…
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