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arXiv · 0909.5190

Constraints on primordial isocurvature perturbations and spatial curvature by Bayesian model selection

Abstract

We present posterior likelihoods and Bayesian model selection analysis for generalized cosmological models where the primordial perturbations include correlated adiabatic and cold dark matter isocurvature components. We perform nested sampling with flat and, for the first time, curved spatial geometries of the Universe, using data from the cosmic microwave background (CMB) anisotropies, the Union supernovae (SN) sample and a combined measurement of the integrated Sachs-Wolfe (ISW) effect. The CMB alone favors a 3% (positively correlated) isocurvature contribution in both the flat and curved cases. The non-adiabatic contribution to the observed CMB temperature variance is 0 < alpha_T < 7% at 98% CL in the curved case. In the flat case, combining the CMB with SN data artificially biases the result towards the pure adiabatic LCDM concordance model, whereas in the curved case the favored level of non-adiabaticity stays at 3% level with all combinations of data. However, the ratio of Bayes factors, or Delta ln(evidence), is more than 5 points in favor of the flat adiabatic LCDM model, which suggests that the inclusion of the 5 extra parameters of the curved isocurvature model is not supported by the current data. The results are very sensitive to the second and third acoustic peak regions in the CMB temperature angular power: therefore a careful calibration of these data will be required before drawing decisive conclusions on the nature of primordial perturbations. Finally, we point out that the odds for the flat non-adiabatic model are 1:3 compared to the curved adiabatic model. This may suggest that it is not much less motivated to extend the concordance model with 4 isocurvature degrees of freedom than it is to study the spatially curved adiabatic model.

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BibTeXRIS

Jussi Valiviita, Tommaso Giannantonio. 2009-12-02. Constraints on primordial isocurvature perturbations and spatial curvature by Bayesian model selection. https://doi.org/10.1103/physrevd.80.123516

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