Status on bidimensional dark energy parameterizations using SNe Ia JLA and BAO datasets

TL;DR

This study evaluates six bidimensional dark energy models using SNe Ia JLA and BAO data, finding that models with $z^2$-terms reduce dataset tension and are mostly compatible with $ ext{Lambda}$CDM, with Bayesian evidence favoring $ ext{Lambda}$CDM.

Contribution

It compares six bidimensional dark energy parameterizations using combined SNe Ia and BAO datasets, highlighting models with $z^2$-terms and their compatibility with $ ext{Lambda}$CDM.

Findings

Models with $z^2$-terms reduce dataset tension.

Most models are compatible with $ ext{Lambda}$CDM within $1\sigma$.

Bayesian evidence strongly favors $ ext{Lambda}$CDM.

Abstract

Using current observations forecast type Ia supernovae (SNe Ia) Joint Lightcurve Analysis (JLA) and baryon acoustic oscillations (BAO), in this paper we investigate six bidimensional dark energy parameterisations in order to explore which has more constraining power. Our results indicate that for parameterisations that contain $z^2$-terms, the tension ($\sigma$-distance) between these datasets seems to be reduced and their behaviour are $<$1$\sigma$ compatible with $\Lambda$CDM. Also, the results obtained by performing their Bayesian evidence show a striking evidence in favour of the $\Lambda$CDM model, but only one parameterisation can be distinguish by around $1\%$ from the other models when the combination of datasets are considered.