4 research outputs found
Model selection forecasts for the spectral index from the Planck satellite
The recent WMAP3 results have placed measurements of the spectral index n_S
in an interesting position. While parameter estimation techniques indicate that
the Harrison-Zel'dovich spectrum n_S=1 is strongly excluded (in the absence of
tensor perturbations), Bayesian model selection techniques reveal that the case
against n_S=1 is not yet conclusive. In this paper, we forecast the ability of
the Planck satellite mission to use Bayesian model selection to convincingly
exclude (or favour) the Harrison-Zel'dovich model.Comment: 4 pages RevTeX with one figure included. Updated to match PRD
accepted version. Improved likelihood function implementation; no qualitative
change to results but some tiny numerical shift
Bayesian analysis of Friedmannless cosmologies
Assuming only a homogeneous and isotropic universe and using both the 'Gold'
Supernova Type Ia sample of Riess et al. and the results from the Supernova
Legacy Survey, we calculate the Bayesian evidence of a range of different
parameterizations of the deceleration parameter. We consider both spatially
flat and curved models. Our results show that although there is strong evidence
in the data for an accelerating universe, there is little evidence that the
deceleration parameter varies with redshift.Comment: 7 pages, 3 figure
A Bayesian model selection analysis of WMAP3
We present a Bayesian model selection analysis of WMAP3 data using our code
CosmoNest. We focus on the density perturbation spectral index and the
tensor-to-scalar ratio , which define the plane of slow-roll inflationary
models. We find that while the Bayesian evidence supports the conclusion that
, 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 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 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.Comment: 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.or