Non-Gaussian inference from non-linear and non-Poisson biased distributed data

TL;DR

This paper develops a Bayesian inference method to reconstruct the dark matter density field from complex, non-Gaussian, and biased tracers, validated with simulated data.

Contribution

It introduces a novel Bayesian sampling approach tailored for non-Gaussian, non-linear, and biased data in cosmology, with implementation and testing on mock simulations.

Findings

Successful reconstruction of dark matter density from mock data

Demonstrates effectiveness of Bayesian sampling in complex data scenarios

Provides a new tool for cosmological data analysis

Abstract

We study the statistical inference of the cosmological dark matter density field from non-Gaussian, non-linear and non-Poisson biased distributed tracers. We have implemented a Bayesian posterior sampling computer-code solving this problem and tested it with mock data based on N-body simulations.