Built-in precision: Improving cluster cosmology through the self-calibration of a galaxy cluster sample

Abstract

We examine the potential improvements in constraints on the dark energy equation of state parameter $w$ and matter density ${\Omega }_M$ from using clustering information along with number counts for future samples of thermal Sunyaev-Zel'dovich selected galaxy clusters. We quantify the relative improvement from including the clustering power spectrum information for three cluster sample sizes from 33,000 to 140,000 clusters and for three assumed priors on the mass slope and redshift evolution of the mass-observable relation. As expected, clustering information has the largest impact when (i) there are more clusters and (ii) the mass-observable priors are weaker. For current knowledge of the cluster mass-observable relationship, we find the addition of clustering information reduces the uncertainty on the dark energy equation of state, $\sigma (w)$, by factors of $1.023\pm 0.007$ to $1.0790\pm 0.011$, with larger improvements observed with more clusters. Clustering information is more important for the matter density, with $\sigma ({\Omega }_M)$ reduced by factors of $1.068\pm 007$ to $1.145\pm 0.012$. The improvement in $w$ constraints from adding clustering information largely vanishes after tightening priors on the mass-observable relationship by a factor of two. For weaker priors, we find clustering information improves the determination of the cluster mass slope and redshift evolution by factors of $1.389\pm 0.041$ and $1.340\pm 0.039$ respectively. These findings highlight that, with the anticipated surge in cluster detections from next generation surveys, self-calibration through clustering information will provide an independent cross-check on the mass slope and redshift evolution of the mass-observable relationship as well as enhancing the precision achievable from cluster cosmology.

8 pages, 3 figures, 1 Table, accepted for publication in Publications of the Astronomical Society of Australia (PASA)

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