Does dark energy really revive using DESI 2024 data?
Youri Carloni, Orlando Luongo, Marco Muccino

TL;DR
This study analyzes DESI 2024 data to evaluate dark energy models, finding that a complex log-corrected model fits better than standard parametrizations and that future data is crucial to resolve tensions with the ΛCDM model.
Contribution
It introduces a comprehensive analysis of multiple dark energy models using DESI 2024 data, highlighting the potential of complex models to better fit observations.
Findings
A log-corrected dark energy model outperforms CPL parametrization.
Removing specific data points aligns results with ΛCDM.
Future data are essential to confirm dynamical dark energy presence.
Abstract
We investigate the impact of the Dark Energy Spectroscopic Instrument (DESI) 2024 data on dark energy scenarios. We thus analyze three typologies of models, the first in which the cosmic speed up is related to thermodynamics, the second associated with Taylor expansions of the barotropic factor, whereas the third based on \emph{ad hoc} dark energy parameterizations. In this respect, we perform Monte Carlo Markov chain analyses, adopting the Metropolis-Hastings algorithm, of 12 models. To do so, we first work at the background, inferring \emph{a posteriori} kinematic quantities associated with each model. Afterwards, we obtain early time predictions, computing departures on the growth evolution with respect to the model that better fits DESI data. We find that the best model to fit data \emph{is not} the Chevallier-Polarski-Linder (CPL) parametrization, but rather a more complicated…
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Taxonomy
TopicsCosmology and Gravitation Theories · Dark Matter and Cosmic Phenomena · Astronomy and Astrophysical Research
