Dynamical dark energy in the light of DESI 2024 data

TL;DR

This paper introduces an alternative parameterization of dark energy's equation of state, showing it fits recent DESI 2024 data better than the standard model, reduces Hubble tension, and supports dynamical dark energy with evidence of phantom barrier crossing.

Contribution

It proposes a new two-parameter dark energy EoS model that improves data fit, reduces Hubble tension, and favors dynamical dark energy over the cosmological constant.

Findings

Supports dynamical dark energy with phantom barrier crossing.

Reduces Hubble tension to 2.8σ with HST and SH0ES data.

Bayesian analysis favors the new model over ΛCDM.

Abstract

The latest findings from the DESI (Dark Energy Spectroscopic Instrument) data release 1 (DR1) [1], combined with data from the cosmic microwave background and supernovae, suggest a preference for dynamical dark energy over the cosmological constant. This study has considered the Chevallier-Polarski- Linder (CPL) parameterization for the dark energy equation of state (EoS) and has indicated a possible phantom barrier crossing in the recent past. Despite CPL being the most commonly used parameterization, recent research has pointed out issues with its prior selection and parameter degeneracies. In this paper, we propose an alternative two-parameter parameterization of the dark energy equation of state (EoS). At higher redshifts, it behaves like the cosmological constant. At redshifts z < 1, this parameterization closely approximates the CPL form but deviates from it at lower redshifts. Our findings also indicate that the current value of the EoS of dark energy resembles quintessence, with evidence of a recent crossing of the phantom barrier, supporting the conclusions in [1]. Furthermore, our model significantly reduces the Hubble tension to about 2.8{\sigma} when compared to Hubble Space Telescope and SH0ES data [2], and to 1.6{\sigma} with standardized TRGB and Type Ia supernova data [3]. Bayesian model selection using Bayes factors and Akaike Information Criteria (ACI), shows a strong preference for our parameterization over the {\Lambda} CDM model, aligned with the DESI2024 results and favoring dynamical dark energy.