Gaussian discriminators between $\Lambda$CDM and wCDM cosmologies using expansion data

TL;DR

This paper demonstrates how Gaussian linear models can analytically discriminate between $ m ext{Λ}$CDM and wCDM cosmologies using expansion data with varying precision, providing a new method for model selection.

Contribution

It introduces an analytical approach using Gaussian linear models to discriminate cosmological models based on expansion data, including Bayesian evidence calculation and comparison with MCMC inference.

Findings

Can distinguish w=-1.02 from ΛCDM with 0.5% data uncertainty

Gaussian model provides analytical Bayesian evidence for model comparison

Results consistent with MCMC parameter inference

Abstract

The Gaussian linear model provides a unique way to obtain the posterior probability distribution as well as the Bayesian evidence analytically. Considering the expansion rate data, the Gaussian linear model can be applied for $\Lambda$CDM, wCDM, and a non-flat $\Lambda$CDM. In this paper, we simulate the expansion data with various precision and obtain the Bayesian evidence, then it has been used to discriminate the models. The data uncertainty is in the range $\sigma\in(0.5,10)\%$ and two different sampling rates have been considered. Our results indicate that it is possible to discriminate $w=-1.02$ (or $w=-0.98$) model from the $\Lambda$CDM $(w=-1)$ with $\sigma=0.5\%$ uncertainty in expansion rate data. Finally, we perform a parameters inference in both the MCMC and Gaussian linear model, using currently available expansion rate data, and compare the results.