Euclid preparation

Abstract

We validate a semi-analytical model for the covariance of the real-space two-point correlation function of galaxy clusters. Using 1000 PINOCCHIO light cones mimicking the expected sample of galaxy clusters, we calibrated a simple model to accurately describe the clustering covariance. Then, we used this model to quantify the likelihood-analysis response to variations in the covariance, and we investigated the impact of a cosmology-dependent matrix at the level of statistics expected for the survey of galaxy clusters. We find that a Gaussian model with Poissonian shot-noise does not correctly predict the covariance of the two-point correlation function of galaxy clusters. By introducing a few additional parameters fitted from simulations, the proposed model reproduces the numerical covariance with an accuracy of 10\,<!PCT!>, with differences of about 5\,<!PCT!> on the figure of merit of the cosmological parameters $ m $ and $ We also find that the covariance contains additional valuable information that is not present in the mean value, and the constraining power of cluster clustering can improve significantly when its cosmology dependence is accounted for. Finally, we find that the cosmological figure of merit can be further improved when mass binning is taken into account. Our results have significant implications for the derivation of cosmological constraints from the two-point clustering statistics of the survey of galaxy clusters.