Neural Network Reconstruction of Late-Time Cosmology and Null Tests

TL;DR

This paper investigates using artificial neural networks for nonparametric reconstruction of late-time cosmological parameters from observational data, addressing issues in Gaussian process methods and testing the concordance model.

Contribution

It introduces a neural network-based approach for reconstructing cosmological parameters and performs null tests to validate the standard cosmological model.

Findings

Neural networks can effectively reconstruct cosmological parameters from data.

Mild deviations from the concordance model are observed with cosmic growth data.

The method addresses overfitting and kernel issues present in Gaussian process reconstructions.

Abstract

The prospect of nonparametric reconstructions of cosmological parameters from observational data sets has been a popular topic in the literature for a number of years. This has mainly taken the form of a technique based on Gaussian processes but this approach is exposed to several foundational issues ranging from overfitting to kernel consistency problems. In this work, we explore the possibility of using artificial neural networks (ANN) to reconstruct late-time expansion and large scale structure cosmological parameters. We first show how mock data can be used to design an optimal ANN for both parameters, which we then use with real data to infer their respective redshift profiles. We further consider cosmological null tests with the reconstructed data in order to confirm the validity of the concordance model of cosmology, in which we observe a mild deviation with cosmic growth data.