Gaussian discriminators between ΛCDM and wCDM cosmologies using expansion data

Abstract

The Gaussian linear model provides a unique way to obtain the posterior probability distribution as well as the Bayesian evidence analytically. Considering the expansion rate data, the Gaussian linear model can be applied for ΛCDM, wCDM and a non-flatΛCDM. In this paper,we simulate the expansion data with various precision and obtain the Bayesian evidence, then it has been used to discriminate the models. The data uncertainty is in range σ ∈ (0.5, 10)% and two different sampling rates have been considered. Our results indicate that considering σ = 0.5% uncertainty, it is possible to discriminate 2% deviation in equation of state from w = −1. On the other hand, we investigate how precision of the expansion rate data affects discriminating the ΛCDM from a non-flat ΛCDM model. Finally, we perform a parameters inference in both the MCMC and Gaussian linear model, using current available expansion rate data and compare the results.