Combining cluster number counts and galaxy clustering
Fabien Lacasa, Rogerio Rosenfeld

TL;DR
This paper models the joint covariance of galaxy cluster counts and galaxy clustering, demonstrating that accounting for non-Gaussian effects enhances the precision of cosmological and galaxy-halo connection parameter constraints in large-scale surveys.
Contribution
It provides a detailed halo model-based framework for joint covariance analysis, including non-Gaussian effects like super-sample covariance, improving parameter constraints from combined probes.
Findings
Non-Gaussian covariance dominates at small scales and low redshifts.
Combining cluster counts and galaxy clustering improves parameter constraints by up to 20%.
Cross-covariance introduces significant synergy, reducing errors on HOD parameters by up to 4.8 times.
Abstract
The abundance of clusters and the clustering of galaxies are two of the important cosmological probes for current and future large scale surveys of galaxies, such as the Dark Energy Survey. In order to combine them one has to account for the fact that they are not independent quantities, since they probe the same density field. It is important to develop a good understanding of their correlation in order to extract parameter constraints. We present a detailed modelling of the joint covariance matrix between cluster number counts and the galaxy angular power spectrum. We employ the framework of the halo model complemented by a Halo Occupation Distribution model (HOD). We demonstrate the importance of accounting for non-Gaussianity to produce accurate covariance predictions. Indeed, we show that the non-Gaussian covariance becomes dominant at small scales, low redshifts or high cluster…
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