The CMB angular power spectrum via component separation: a study on Planck data

TL;DR

This study assesses the effectiveness of the SMICA component separation method on Planck data for small-scale CMB analysis, focusing on foreground removal and its impact on cosmological parameter estimation.

Contribution

It adapts SMICA to use only cross-spectra, enabling unbiased small-scale CMB power spectrum estimation and detailed modeling of extragalactic point source contamination.

Findings

Successfully recover point source emission law in simulations.

Achieve residual foreground contamination at 1/5 of CMB power for ll geq 2200.

Cosmological parameters show up to 1c bias consistent with simulations.

Abstract

We investigate the extent to which foreground cleaned CMB maps can be used to estimate the cosmological parameters at small scales. We use the SMICA method, a blind separation technique which works directly at the spectral level. In this work we focus on the small scales of the CMB angular power spectrum, which are chiefly affected by noise and extragalactic foregrounds, such as point sources. We adapt SMICA to use only cross-spectra between data maps, thus avoiding the noise bias. In this study, performed both on simulations and on Planck 2015 data, we fit for extragalactic point sources by modeling them as shot noise of two independent populations. In simulations we correctly recover the point source emission law, and obtain a CMB angular power spectrum that has an average foreground residual of one fifth of the CMB power at $\ell \geq$ 2200. On Planck data, the recovered point source emission law corresponds to external estimates, with some offsets at the highest and lowest frequencies, possibly due to frequency decoherence of point sources. The CMB angular power spectrum residuals are consistent with what we find in simulations. The cosmological parameters obtained from the simulations and the data show offsets up to 1$\sigma$ on average from their expected values. Biases on cosmological parameters in simulations represent the expected level of bias in Planck data. The results on cosmological parameters depend on the detail of the foreground residual contamination in the spectrum, and need a tailored modeling of the likelihood foreground model.