Optimizing spectral-matching component separation for measuring CMB B-mode polarization with a satellite mission

Abstract

The measurement of primordial CMB B-mode polarization is fundamentally limited by our ability to separate this faint signal from bright and complex Galactic foregrounds. Spectral Matching Independent Component Analysis (SMICA) offers a flexible, semi-blind approach to this foreground-cleaning problem. However, its standard implementation relies on a physically unrealistic assumption of non-spatially varying foreground emission, reducing the efficiency of the method. In this work, we develop two complementary strategies to address this limitation at the component-separation level. First, we augment SMICA with effective foreground components beyond the physical dust and synchrotron degrees of freedom. Second, we extend SMICA to operate independently on subsets of the sky, better capturing local variations in the foreground emission. Beyond component separation, we further explore a likelihood-level marginalization procedure using internally reconstructed foreground templates. We demonstrate the performance of the pipeline on simulated LiteBIRD-like observations, using foreground models of increasing complexity based on $\texttt{d1s1}$ and $\texttt{d10s5}$. Combining our component-separation improvements with template marginalization brings the recovered tensor-to-scalar ratio $r$ to effectively unbiased levels, with statistical uncertainties on the order of $10^{-3}$ or below.