The observational constraints on the flat $\phi$CDM models

TL;DR

This paper assesses how well various flat $\phi$CDM dark energy models fit upcoming observational data, finding that the $\Lambda$CDM model is generally favored, and explores the CPL parametrization's effectiveness.

Contribution

It provides a comprehensive Bayesian analysis of multiple dark energy models against future DESI data, highlighting the robustness of $\Lambda$CDM and evaluating CPL parametrization accuracy.

Findings

Bayes factor favors $\Lambda$CDM over alternatives

CPL parametrization approximates scalar field models with varying accuracy

Future DESI data will strongly constrain dark energy models

Abstract

Most dark energy models have the $\Lambda$CDM as their limit, and if future observations constrain our universe to be close to $\Lambda$CDM Bayesian arguments about the evidence and the fine-tuning will have to be employed to discriminate between the models. Assuming a baseline $\Lambda$CDM model we investigate a number of quintessence and phantom dark energy models, and we study how they would perform when compared to observational data, such as the expansion rate, the angular distance, and the growth rate measurements, from the upcoming Dark Energy Spectroscopic Instrument (DESI) survey. We sample posterior likelihood surfaces of these dark energy models with Monte Carlo Markov Chains while using central values consistent with the Planck $\Lambda$CDM universe and covariance matrices estimated with Fisher information matrix techniques. We find that for this setup the Bayes factor provides a substantial evidence in favor of the $\Lambda$CDM model over most of the alternatives. We also investigated how well the CPL parametrization approximates various scalar field dark energy models, and identified the location for each dark energy model in the CPL parameter space.