KASI AI Hub KASI AI Hub

Extended dark energy analysis using DESI DR2 BAO measurements

Lodha et al. (2025)

Kushal Lodha #1 Lead Rodrigo Calderon Bruni #2 Co-author William Luke Matthewson #3 Co-author Arman Shafieloo #4 Corresponding David Parkinson #12 Co-author
PHYSICAL REVIEW D IF 5 A 등급 International collaboration
HPC(High-Performance Clusters)

Abstract

We conduct an extended analysis of dark energy constraints, in support of the findings of the Dark Energy Spectroscopic Instrument (DESI) second data release cosmology key paper, including DESI data, Planck cosmic microwave background observations, and three different supernova compilations. Using a broad range of parametric and nonparametric methods, we explore the dark energy phenomenology and find consistent trends across all approaches, in good agreement with the w0waCDM (cold dark matter) key paper results. Even with the additional flexibility introduced by nonparametric approaches, such as binning and Gaussian processes, we find that extending ΛCDM to include a two-parameter wðzÞ is sufficient to capture the trends present in the data. Finally, we examine three dark energy classes with distinct dynamics, including quintessence scenarios satisfying w ≥ -1, to explore what underlying physics can explain such deviations. The current data indicate a clear preference for models that feature a phantom crossing; although alternatives lacking this feature are disfavored, they cannot yet be ruled out. Our analysis confirms that the evidence for dynamical dark energy, particularly at low redshift (z - 0.3), is robust and stable under different modeling choices.

AI Classification

Maturity Tier

Tier 1 — Classical ML

AI Role

parameter-estimation

supporting

Techniques

gaussian-processclassical-ML

Tasks

regression/parameter-estimation

Subfield

cosmology

tabular-catalog

Flags

Develops AI method
· Classification confidence: 90%