Bayesian analysis of the backreaction models

TL;DR

This paper evaluates backreaction cosmological models using Bayesian analysis and observational data, comparing them to the standard LambdaCDM model, and explores how assumptions about curvature relations affect model fit.

Contribution

It introduces a modified assumption about the relation between curvature and curvature index in backreaction models, improving their fit to observational data.

Findings

LambdaCDM is favored over backreaction models with current data.

Adjusting the curvature-curvature index relation improves backreaction model fit.

Further investigation into the curvature-backreaction relation is necessary.

Abstract

We present the Bayesian analysis of four different types of backreation models, which are based on the Buchert equations. In this approach, one considers a solution to the Einstein equations for a general matter distribution and then an average of various observable quantities is taken. Such an approach became of considerable interest when it was shown that it could lead to agreement with observations without resorting to dark energy. In this paper we compare the LambdaCDM model and the backreation models with SNIa, BAO, and CMB data, and find that the former is favoured. However, the tested models were based on some particular assumptions about the relation between the average spatial curvature and the backreaction, as well as the relation between the curvature and curvature index. In this paper we modified the latter assumption, leaving the former unchanged. We find that, by varying the relation between the curvature and curvature index, we can obtain a better fit. Therefore, some further work is still needed -- in particular the relation between the backreaction and the curvature should be revisited in order to fully determine the feasibility of the backreaction models to mimic dark energy.