Self-Calibrated Cluster Counts as a Probe of Primordial Non-Gaussianity

TL;DR

This paper demonstrates that incorporating excess variance in cluster counts significantly enhances constraints on primordial non-Gaussianity, breaking degeneracies and achieving competitive precision with future surveys.

Contribution

The study introduces a self-calibrated method using count variance to improve primordial non-Gaussianity constraints from galaxy cluster counts.

Findings

Count variance improves f_NL constraints by over an order of magnitude.

Method breaks degeneracy between non-Gaussianity and observable-mass relation.

Upcoming surveys can constrain f_NL to about 8, competitive with CMB experiments.

Abstract

We show that the ability to probe primordial non-Gaussianity with cluster counts is drastically improved by adding the excess variance of counts which contains information on the clustering. The conflicting dependences of changing the mass threshold and including primordial non-Gaussianity on the mass function and biasing indicate that the self-calibrated cluster counts well break the degeneracy between primordial non-Gaussianity and the observable-mass relation. Based on the Fisher matrix analysis, we show that the count variance improves constraints on f_NL by more than an order of magnitude. It exhibits little degeneracy with dark energy equation of state. We forecast that upcoming Hyper Suprime-cam cluster surveys and Dark Energy Survey will constrain primordial non-Gaussianity at the level \sigma(f_NL) \sim 8, which is competitive with forecasted constraints from next-generation cosmic microwave background experiments.