Latest data constraint of some parameterized dark energy models

TL;DR

This paper evaluates several parameterized dark energy models using recent cosmological data, employing statistical and geometric diagnostics to distinguish them from each other and from the standard $\\Lambda$CDM model.

Contribution

It provides a comprehensive comparison of dark energy models with latest data, applying multiple diagnostics and information criteria to identify observational preferences.

Findings

$\\Lambda$CDM remains the best fit model.

Models can be distinguished using statefinder, Om diagnostics, and growth factor analysis.

All models show distinguishable geometric and growth behaviors.

Abstract

Using various latest cosmological datasets including Type-Ia supernovae, cosmic microwave background radiation, baryon acoustic oscillations, and estimations of the Hubble parameter, we test some dark energy models with parameterized equations of state and try to distinguish or select observation-preferred models. We obtain the best fitting results of the six models and calculate their values of the Akaike Information Criteria and Bayes Information Criterion. And we can distinguish these dark energy models from each other by using these two information criterions. However, the $\Lambda $CDM model remains the best fit model. Furthermore, we perform geometric diagnostics including statefinder and Om diagnostics to understand the geometric behaviour of the dark energy models. We find that the six DE models can be distinguished from each other and from $\Lambda$CDM, Chaplygin gas, quintessence models after the statefinder and Om diagnostics were performed. Finally, we consider the growth factor of the dark energy models with comparison to $\Lambda $CDM model. Still, we find the models can be distinguished from each other and from $\Lambda $CDM model through the growth factor approximation.