http://arxiv.org/abs/1701.08165

We examine the ability of the $\Lambda$CDM model to simultaneously fit different types of cosmological observations and apply a recently proposed test, based on the Kullback-Leibler divergence, to quantify the tension between different subsets of data. We find a tension between the distance indicators derived from a $\Lambda$CDM model using a combined dataset, and the recent $H_0$ measurements, as well as the high redshift Baryon Acoustic Oscillations (BAO) obtained from the Lyman-$\alpha$ forest spectra. We then allow for a dynamical dark energy (DE) and perform a Bayesian non-parametric reconstruction of the DE equation of state as a function of redshift. We find that the tension with $H_0$ and Lyman-$\alpha$ forest BAO is effectively relieved by a dynamical DE. Although a comparison of the Bayesian evidence for dynamical DE with that of $\Lambda$CDM shows that the tension between the datasets is not sufficiently strong to support a model with more degrees of freedom, we find that an evolving DE is preferred at a $3.5\sigma$ significance level based solely on the improvement in the fit. We also perform a forecast for the upcoming DESI survey and demonstrate that, if the current best fit DE happens to be the true model, it will be decisively supported by the Bayesian evidence.

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G. Zhao, M. Raveri, L. Pogosian, et. al.

Tue, 31 Jan 17

14/58

Comments: 4.5 + 2 pages, 5 figures, 3 tables

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