Cylinders out of a top hat: counts-in-cells for projected densities

TL;DR

This paper develops a large deviation statistical framework to accurately predict the distribution and clustering of cosmic densities in cylindrical volumes, aiding cosmological analysis of photometric survey data.

Contribution

It introduces a novel large deviation approach for predicting densities and clustering in cylinders, validated against simulations, applicable to upcoming galaxy surveys.

Findings

Achieves few percent accuracy in density PDF predictions.

Successfully models density-dependent clustering (cylinder bias).

Validates predictions with Horizon Run 4 simulations.

Abstract

Large deviation statistics is implemented to predict the statistics of cosmic densities in cylinders applicable to photometric surveys. It yields few percent accurate analytical predictions for the one-point probability distribution function (PDF) of densities in concentric or compensated cylinders; and also captures the density-dependence of their angular clustering (cylinder bias). All predictions are found to be in excellent agreement with the cosmological simulation Horizon Run 4 in the quasi-linear regime where standard perturbation theory normally breaks down. These results are combined with a simple local bias model that relates dark matter and tracer densities in cylinders and validated on simulated halo catalogues. This formalism can be used to probe cosmology with existing and upcoming photometric surveys like DES, Euclid or WFIRST containing billions of galaxies.