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

作者：Vardanyan Mihran^*; Trotta Roberto; Silk Joseph

来源：Monthly Notices of the Royal Astronomical Society, 2011, 413(1): L91-L95.

DOI：10.1111/j.1745-3933.2011.01040.x

• methods: statistical
• cosmological parameters
• cosmology: theory

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 similar to 2. The curvature scale of the Universe is conservatively constrained to be R-c > 42 Gpc (99 per cent), corresponding to a lower limit to the number of Hubble spheres in the Universe N-U > 251 (99 per cent).

• 出版日期2011-5