Super-sample covariance of the thermal Sunyaev-Zel'dovich effect

TL;DR

This paper investigates the impact of super-sample covariance on the thermal Sunyaev-Zel'dovich effect power spectrum, highlighting how removing massive clusters affects variance and improves cosmological parameter constraints.

Contribution

It demonstrates that removing detected massive clusters reduces non-Gaussian covariance, making diffuse tSZ measurements more effective for cosmological inference.

Findings

Super-sample covariance is dominated by non-Gaussian covariance from massive clusters.

Removing massive clusters significantly reduces sample variance.

Diffuse tSZ power spectrum can tightly constrain cosmological parameters.

Abstract

The thermal Sunyaev-Zel'dovich (tSZ) effect is a powerful probe of cosmology. The statistical errors in the tSZ power spectrum measurements are dominated by the presence of massive clusters in a survey volume that are easy to identify on individual cluster basis. First, we study the impact of super sample covariance (SSC) on the tSZ power spectrum measurements, and find that the sample variance is dominated by the connected non-Gaussian (cNG) covariance arising mainly from Poisson number fluctuations of massive clusters in the survey volume. Second, we find that removing such individually-detected, massive clusters from the analysis significantly reduces the cNG contribution, thereby leading the SSC to be a leading source of the sample variance. We then show, based on Fisher analysis, that the power spectrum measured from the remaining diffuse tSZ effects can be used to obtain tight constraints on cosmological parameters as well as the hydrostatic mass bias parameter. Our method offers complementary use of individual tSZ cluster counts and the power spectrum measurements of diffuse tSZ signals for cosmology and intracluster gas physics.