Conformal Holographic Dark Energy

TL;DR

This paper introduces a conformal holographic dark energy model with a parameterized dependence on conformal time, which better fits current cosmological data than LambdaCDM and aligns with neutrino mass constraints.

Contribution

The study proposes and tests a novel CHDE model with a single extra parameter, demonstrating its compatibility with observational data and its potential to resolve tensions in cosmological parameters.

Findings

CHDE model fits data better than LambdaCDM.

Parameter n is constrained to approximately -0.3.

Model predicts neutrino masses near the minimal allowed range.

Abstract

Recent results from the DESI collaboration suggest a preference for an evolving dark energy (DE) component rather than a cosmological constant, motivating the exploration of alternative models for the background expansion. These data also reveal tension in the inferred matter density parameter -- lower in DESI and higher in Planck -- as well as a neutrino mass posterior that approaches the lower bounds permitted by oscillation experiments. In this work, we propose and test a conformal holographic DE (CHDE) model in which the DE density depends on a power law of the conformal time, characterized by an exponent (n). This formulation introduces a single additional parameter relative to LambdaCDM and reduces to it in the limit n = 0. We confront the CHDE model with BAO, CMB, and supernova datasets, following the same combinations used by DESI, and perform parameter inference under both flat and non-flat cosmologies. Our analyses show that LambdaCDM is not favored as the best-fit model when using CMB data alone or in joint analyses including BAO and SNla, and it is disfavored at the 4.4 sigma level for non-flat model and 4.5 sigma for the flat model. We obtain consistent values of n= -0.28 to -0.32 with uncertainties less than +-0.1 across multiple data combinations. Similar to LambdaCDM, the CHDE model predicts a lower matter density when employing DESI data instead of Planck data. This, in turn, influences the neutrino mass constraints, yielding values close to the minimal allowed range. Despite these dataset-dependent tensions, both the flat and curved CHDE models remain compatible with neutrino mass constraints from terrestrial experiments and yield posterior distributions that peaks at positive values. This behavior avoids the issue encountered in the LambdaCDM model, where the posterior peaks at negative mass values.