Gaussian discriminators between varLambda CDM 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 varLambda CDM, wCDM and a non-flat varLambda CDM. 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 sigma in (0.5,10)% and two different sampling rates have been considered. Our results indicate that considering sigma =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 varLambda CDM from a non-flat varLambda CDM 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.