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.
G. Zhao, M. Raveri, L. Pogosian, et. al.
Tue, 31 Jan 17
Comments: 4.5 + 2 pages, 5 figures, 3 tables