Model-independent cosmological inference post DESI DR1 BAO measurements
Purba Mukherjee, Anjan Ananda Sen

TL;DR
This paper uses Gaussian process regression to reconstruct the universe's expansion history in a model-independent way, analyzing various data sets to explore dark energy properties and potential tensions in cosmological measurements.
Contribution
It introduces a model-agnostic reconstruction method combining multiple data sets, revealing insights into dark energy evolution and discrepancies in Hubble parameter measurements.
Findings
Evidence that DESI LRG data at z=0.51 is not an outlier.
Hints of a quintessence-like dark energy with slowly varying EoS.
Approximately 2σ tension between different data set combinations.
Abstract
In this work, we implement Gaussian process regression to reconstruct the expansion history of the universe in a model-agnostic manner, using the Pantheon-Plus SN-Ia compilation in combination with two different BAO measurements (SDSS-IV and DESI DR1). In both the reconstructions, the CDM model is always included in the 95\% confidence intervals. We find evidence that the DESI LRG data at is not an outlier within our model-independent framework. We study the -diagnostics and the evolution of the total equation of state (EoS) of our universe, which hint towards the possibility of a quintessence-like dark energy scenario with a very slowly varying EoS, and a phantom-crossing in higher . The entire exercise is later complemented by considering two more SN-Ia compilations - DES-5YR and Union3 - in combination with DESI BAO. Reconstruction…
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Taxonomy
TopicsStatistical and numerical algorithms · Particle Detector Development and Performance · Cosmology and Gravitation Theories
MethodsHierarchical Information Threading · Gaussian Process
