Comparing dark energy models with current observational data

TL;DR

This study compares thirteen dark energy models using current observational data and various statistical methods, identifying the most favored models and assessing the impact of different analysis techniques on parameter estimation.

Contribution

It provides a systematic comparison of dark energy models with multiple data sets and analysis methods, highlighting the most supported models and the effectiveness of the improved flux statistic.

Findings

The cosmological constant model is most favored by current data.

The improved flux statistic (IFS) provides the strongest constraints on dark energy models.

Different analysis techniques significantly affect parameter estimates and model comparison metrics.

Abstract

We make a comparison for thirteen dark energy (DE) models by using current cosmological observations, including type Ia supernova, baryon acoustic oscillations, and cosmic microwave background. To perform a systematic and comprehensive analysis, we consider three statistics methods of SNIa, including magnitude statistic (MS), flux statistic (FS), and improved flux statistic (IFS), as well as two kinds of BAO data. In addition, Akaike information criteria (AIC) and Bayesian information criteria (BIC) are used to assess the worth of each model. We find that: (1) The thirteen models can be divided into four grades by performing cosmology-fits. The cosmological constant model, which is most favored by current observations, belongs to grade one; $\alpha$DE, constant $w$ and generalized Chaplygin gas models belong to grade two; Chevalliear-Polarski-Linder (CPL) parametrization, Wang parametrization, doubly coupled massive gravity, new generalized Chaplygin gas and holographic DE models belong to grade three; agegraphic DE, Dvali-Gabadadze-Porrati, Vacuum metamorphosis and Ricci DE models, which are excluded by current observations, belong to grade four. (2) For parameter estimation, adopting IFS yields the biggest $\Omega_m$ and the smallest $h$ for all the models. In contrast, using different BAO data does not cause significant effects. (3) IFS has the strongest constraint ability on various DE models. For examples, adopting IFS yields the smallest value of $\Delta$AIC for all the models; in addition, making use of this technique yields the biggest figure of merit for CPL and Wang parametrizations.