Robust Preference for Dynamical Dark Energy in DESI BAO and SN Measurements

TL;DR

This study confirms that recent DESI BAO and supernova data favor a dynamical dark energy model with a quintessence-like present state and a past evolution into the phantom regime, robust across different parameterizations.

Contribution

The paper tests the impact of different dark energy parameterizations on the preference for dynamical dark energy, showing robustness and identifying the Barboza-Alcaniz model as most fitting.

Findings

Preference for dynamical dark energy remains robust across models.

$w_0$ stays in the quintessence regime, $w_a$ indicates evolution towards phantom.

Barboza-Alcaniz model provides the best fit among tested models.

Abstract

Recent Baryon Acoustic Oscillation (BAO) measurements released by DESI, when combined with Cosmic Microwave Background (CMB) data from Planck and two different samples of Type Ia supernovae (Pantheon-Plus and DESY5) reveal a preference for Dynamical Dark Energy (DDE) characterized by a present-day quintessence-like equation of state that crossed into the phantom regime in the past. A core ansatz for this result is assuming a linear Chevallier-Polarski-Linder (CPL) parameterization $w(a) = w_0 + w_a (1-a)$ to describe the evolution of the DE equation of state (EoS). In this paper, we test if and to what extent this assumption impacts the results. To prevent broadening uncertainties in cosmological parameter inference and facilitate direct comparison with the baseline CPL case, we focus on 4 alternative well-known models that, just like CPL, consist of only two free parameters: the present-day DE EoS ($w_0$) and a parameter quantifying its dynamical evolution ($w_a$). We demonstrate that the preference for DDE remains robust regardless of the parameterization: $w_0$ consistently remains in the quintessence regime, while $w_a$ consistently indicates a preference for a dynamical evolution towards the phantom regime. This tendency is significantly strengthened by DESY5 SN measurements. By comparing the best-fit $\chi^2$ obtained within each DDE model, we notice that the linear CPL parameterization is not the best-fitting case. Among the models considered, the EoS proposed by Barboza and Alcaniz consistently leads to the most significant improvement.