Applications of Bayesian model averaging to the curvature and size of the Universe

TL;DR

This paper applies Bayesian model averaging to recent cosmological data to derive tighter constraints on the Universe's curvature and size, accounting for model uncertainty and allowing for evolving dark energy.

Contribution

It introduces a Bayesian model averaging approach to improve constraints on the Universe's curvature and size, considering model uncertainty and dark energy evolution.

Findings

Curvature scale R_c > 42 Gpc at 99% confidence

Number of Hubble spheres N_U > 251 at 99% confidence

Model-averaged constraints are stronger than non model-averaged ones

Abstract

Bayesian model averaging is a procedure to obtain parameter constraints that account for the uncertainty about the correct cosmological model. We use recent cosmological observations and Bayesian model averaging to derive tight limits on the curvature parameter, as well as robust lower bounds on the curvature radius of the Universe and its minimum size, while allowing for the possibility of an evolving dark energy component. Because flat models are favoured by Bayesian model selection, we find that model-averaged constraints on the curvature and size of the Universe can be considerably stronger than non model-averaged ones. For the most conservative prior choice (based on inflationary considerations), our procedure improves on non model-averaged constraints on the curvature by a factor of ~ 2. The curvature scale of the Universe is conservatively constrained to be R_c > 42 Gpc (99%), corresponding to a lower limit to the number of Hubble spheres in the Universe N_U > 251 (99%).