Applications of Bayesian model averaging to the curvature and size of the Universe
arXiv:1101.5476 · doi:10.1111/j.1745-3933.2011.01040.x
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%).
Fixed typos, added ref, added non model-averaged constraints for comparison. Matches version accepted by MNRAS Lett
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