Combination of cluster number counts and two-point correlations: Validation on Mock Dark Energy Survey

TL;DR

This paper introduces a combined analysis method of cluster counts and two-point correlations, validated on mock DES-Y1 data, achieving competitive cosmological constraints without small-scale lensing calibration.

Contribution

It presents the first end-to-end validation of a combined cluster and galaxy correlation analysis on galaxy catalog simulations for cosmology.

Findings

Validation shows no significant systematic biases.

The combined data vector yields constraints comparable to traditional methods.

Robustness confirmed against various systematic effects.

Abstract

We present a method of combining cluster abundances and large-scale two-point correlations, namely galaxy clustering, galaxy--cluster cross-correlations, cluster auto-correlations, and cluster lensing. This data vector yields comparable cosmological constraints to traditional analyses that rely on small-scale cluster lensing for mass calibration. We use cosmological survey simulations designed to resemble the Dark Energy Survey Year One (DES-Y1) data to validate the analytical covariance matrix and the parameter inferences. The posterior distribution from the analysis of simulations is statistically consistent with the absence of systematic biases detectable at the precision of the DES Y1 experiment. We compare the $\chi^2$ values in simulations to their expectation and find no significant difference. The robustness of our results against a variety of systematic effects is verified using a simulated likelihood analysis of a Dark Energy Survey Year 1-like data vectors. This work presents the first-ever end-to-end validation of a cluster abundance cosmological analysis on galaxy catalog-level simulations.