CMB at small scales: Cosmology from tSZ power spectrum

TL;DR

This paper presents a new analysis of small-scale CMB data focusing on the thermal Sunyaev Zel'dovich effect, using a halo model and machine learning to improve cosmological parameter constraints.

Contribution

It introduces a novel approach combining halo modeling and random forest algorithms to analyze tSZ data, enhancing cosmological parameter estimation from CMB observations.

Findings

Incorporating tSZ data improves constraints on cosmological parameters.

Combining Planck and SPT tSZ spectra yields tighter bounds.

Proper foreground modeling is crucial for accurate cosmological analysis.

Abstract

Small scale CMB angular power spectrum contains not only primordial CMB information but also many contaminants coming from secondary anisotropies. Most of the latter depend on the cosmological model but are often marginalised over. We propose a new analysis of the SPT data focusing on the cosmological contribution of the thermal Sunyaev Zel'dovich (tSZ) effect. We model the tSZ angular spectrum with the halo model and train a random forest algorithm to speed up its computation. We show that using the cosmological information of the tSZ on top of the primordial CMB one contained in SPT data bring more constraints on cosmological parameters. We also combine for the first time Planck tSZ angular power spectrum with SPT ones to put further constraints. This proof of concept study shows how much a proper modelling of the foregrounds in the cosmological analyses is needed.