Combining clustering and abundances of galaxy clusters to test cosmology and primordial non-Gaussianity

TL;DR

This paper demonstrates that galaxy cluster clustering analysis, combined with traditional methods, significantly enhances cosmological parameter constraints and provides new limits on primordial non-Gaussianity.

Contribution

It introduces a method combining cluster clustering, abundances, and power spectrum data to improve cosmological and primordial non-Gaussianity constraints.

Findings

Cluster clustering improves constraints on σ8 and Ωm by ~50%.

The method constrains local non-Gaussianity parameter fNL to 12 ± 157.

Adding power spectrum data enhances self-calibration of scaling relations.

Abstract

We present the clustering of galaxy clusters as a useful addition to the common set of cosmological observables. The clustering of clusters probes the large-scale structure of the Universe, extending galaxy clustering analysis to the high-peak, high-bias regime. Clustering of galaxy clusters complements the traditional cluster number counts and observable-mass relation analyses, significantly improving their constraining power by breaking existing calibration degeneracies. We use the maxBCG galaxy clusters catalogue to constrain cosmological parameters and cross-calibrate the mass-observable relation, using cluster abundances in richness bins and weak-lensing mass estimates. We then add the redshift-space power spectrum of the sample, including an effective modelling of the weakly non-linear contribution and allowing for an arbitrary photometric redshift smoothing. The inclusion of the power spectrum data allows for an improved self-calibration of the scaling relation. We find that the inclusion of the power spectrum typically brings a $\sim 50$ per cent improvement in the errors on the fluctuation amplitude $\sigma_8$ and the matter density $\Omega_{\mathrm{m}}$. Finally, we apply this method to constrain models of the early universe through the amount of primordial non-Gaussianity of the local type, using both the variation in the halo mass function and the variation in the cluster bias. We find a constraint on the amount of skewness $f_{\mathrm{NL}} = 12 \pm 157 $ ($1\sigma$) from the cluster data alone.