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 $$\varLambda $$ Λ CDM, wCDM and a non-flat $$\varLambda $$ Λ 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 $$\sigma \in (0.5,10)\%$$ σ ∈ ( 0.5 , 10 ) % and two different sampling rates have been considered. Our results indicate that considering $$\sigma =0.5\%$$ σ = 0.5 % uncertainty, it is possible to discriminate 2$$\%$$ % deviation in equation of state from $$w=-1$$ w = - 1 . On the other hand, we investigate how precision of the expansion rate data affects discriminating the $$\varLambda $$ Λ CDM from a non-flat $$\varLambda $$ Λ 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.

Model selection results from different BAO datasets -- DE models and $\Omega_K$CDM

“…The errors for both datasets are comparable. Such results have also been found in other model-independent approaches [33,34].…”