A Bayesian model selection analysis of WMAP3

Abstract

We present a Bayesian model selection analysis of WMAP3 data using our code CosmoNest. We focus on the density perturbation spectral index $n_S$ and the tensor-to-scalar ratio $r$, which define the plane of slow-roll inflationary models. We find that while the Bayesian evidence supports the conclusion that $n_S\ne 1$, the data are not yet powerful enough to do so at a strong or decisive level. If tensors are assumed absent, the current odds are approximately 8 to 1 in favour of $n_S\ne 1$ under our assumptions, when WMAP3 data is used together with external data sets. WMAP3 data on its own is unable to distinguish between the two models. Further, inclusion of $r$ as a parameter weakens the conclusion against the Harrison-Zel'dovich case (n_S = 1, r=0), albeit in a prior-dependent way. In appendices we describe the CosmoNest code in detail, noting its ability to supply posterior samples as well as to accurately compute the Bayesian evidence. We make a first public release of CosmoNest, now available at http://www.cosmonest.org.

7 pages RevTex with 4 figures included. Updated to match PRD accepted version. Main results unchanged. CosmoNest code now version 1.0 and includes calculation of the Information. Code available at http://www.cosmonest.org

References in corpus (1)

• Model selection forecasts for the spectral index from the Planck satellite

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