Joint likelihood function of cluster counts and $n$-point correlation functions: Improving their power through including halo sample variance

TL;DR

This paper develops a joint likelihood framework for cluster counts and n-point correlation functions, accounting for non-Gaussian covariance, which enhances cosmological parameter constraints in large-scale structure surveys.

Contribution

It introduces an analytical approach using a halo model to incorporate halo sample variance into joint likelihood analyses of large-scale structure observables.

Findings

Joint analysis recovers significant information lost to non-Gaussian covariance.

Estimated 30-40% improvement in parameter constraints for upcoming surveys.

Analytical model agrees well with simulations.

Abstract

Naive estimates of the statistics of large scale structure and weak lensing power spectrum measurements that include only Gaussian errors exaggerate their scientific impact. Non-linear evolution and finite volume effects are both significant sources of non-Gaussian covariance that reduce the ability of power spectrum measurements to constrain cosmological parameters. Using a halo model formalism, we derive an intuitive understanding of the various contributions to the covariance and show that our analytical treatment agrees with simulations. This approach enables an approximate derivation of a joint likelihood for the cluster number counts, the weak lensing power spectrum and the bispectrum. We show that this likelihood is a good description of the ray-tracing simulation. Since all of these observables are sensitive to the same finite volume effects and contain information about the non-linear evolution, a combined analysis recovers much of the "lost" information and obviates the non-Gaussian covariance. For upcoming weak lensing surveys, we estimate that a joint analysis of power spectrum, number counts and bispectrum will produce an improvement of about $30-40\%$ in determinations of the matter density and the scalar amplitude. This improvement is equivalent to doubling the survey area.