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

¹ Institut d’Astrophysique de Paris, Sorbonne Université, CNRS (UMR7095), 98 bis Boulevard Arago, 75014 Paris, France
e-mail: umiltca@ucmail.uc.edu
² Sorbonne Universités, Institut Lagrange de Paris (ILP), 98 bis Boulevard Arago, 75014 Paris, France
³ University of Cincinnati, Cincinnati, OH 45221, USA
⁴ APC, Univ Paris Diderot, CNRS/IN2P3, CEA/Irfu, Obs de Paris, Sorbonne Paris Cité, France

Received: 2 July 2018
Accepted: 19 October 2018

Abstract

Aims. We investigate the extent to which foreground-cleaned cosmic microwave background (CMB) maps can be used to estimate cosmological parameters at small scales.

Methods. We use the SMICA method, a blind separation technique that 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 using both simulations and Planck 2015 data, we fit for extragalactic point sources by modelling them as shot noise of two independent populations.

Results. 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 ℓ ≥ 2200. With 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 1σ on average from their expected values. Biases on cosmological parameters in simulations represent the expected level of bias in Planck data.

Conclusions. The results on cosmological parameters depend on the detail of the foreground residual contamination in the spectrum, and therefore a tailored modelling of the likelihood foreground model is required.