Constraining the dark energy models using Baryon Acoustic Oscillations: An approach independent of $H_0 \cdot r_d$

Abstract

The $H_0$ tension and the accompanying $r_d$ tension are a hot topic in current cosmology. In order to remove the degeneracy between the Hubble parameter $H_0$ and the sound horizon scale $r_d$ from the Baryon Acoustic Oscillations (BAO) datasets, we redefine the likelihood by marginalizing over the $H_0 \cdot r_d$ parameter and then we perform full Bayesian analysis for different models of dark energy (DE). We find that our uncalibrated by early or late physics {datasets cannot} constrain the DE models properly without further assumptions. By adding the type IA supernova dataset, the models are constrained better with smaller errors on the DE parameters. The two BAO datasets we use -- one with angular measurements and one with angular and radial ones with their covariances, show statistical preferences for different models, with $\Lambda$CDM being the best model for one of them. Adding the Pantheon SnIA dataset with its covariance matrix boosts the statistical preference for $\Lambda$CDM.