Robust, data-driven inference in non-linear cosmostatistics

TL;DR

This paper presents two non-linear cosmostatistics methods: a Bayesian approach for galaxy redshift reconstruction from photometric data and a void-based technique for cosmic expansion measurement, both leveraging non-linear effects.

Contribution

It introduces novel non-linear cosmostatistics methods that improve galaxy redshift reconstruction and cosmic expansion inference using voids and non-linearities.

Findings

Non-linearities enhance the accuracy of galaxy redshift reconstruction.

Void-based methods effectively reconstruct the Universe's expansion history.

Non-linear gravitational effects create observable cosmic voids.

Abstract

We discuss two projects in non-linear cosmostatistics applicable to very large surveys of galaxies. The first is a Bayesian reconstruction of galaxy redshifts and their number density distribution from approximate, photometric redshift data. The second focuses on cosmic voids and uses them to construct cosmic spheres that allow reconstructing the expansion history of the Universe using the Alcock-Paczynski test. In both cases we find that non-linearities enable the methods or enhance the results: non-linear gravitational evolution creates voids and our photo-z reconstruction works best in the highest density (and hence most non-linear) portions of our simulations.