📝 SummarXiv

Abstract

Component separation methods mitigate the cross-contamination between different extragalactic and galactic contributions to cosmic microwave background (CMB) data. This is often done by linearly combining CMB maps from different frequency channels using internal linear combination (ILC) methods. We demonstrate that deriving power spectrum estimators directly by linearly combining auto- and cross-spectra instead of maps allows us to obtain a different constrained-optimization problem that allows fewer (deprojection) constraint equations than combining at map level using the constrained ILC method. Through simulations, we show that our Spectral internal linear combination (SpILC) produces CMB power spectrum estimators with more than 7 times smaller errorbars than constrained ILC (with thermal Sunyaev-Zel'dovich and cosmic infrared background deprojections) at $\ell\gtrsim 4000$ for Simons Observatory-like observations. Spectral ILC outperforms constrained ILC methods when some modeled components are spatially uncorrelated, e.g. the primary CMB is uncorrelated with foregrounds, and the difference in performance is most significant at noise-dominated scales. More generally, our work shows that component-separated maps with foreground deprojections do not necessarily produce minimum-variance two-or-higher-point estimators.