mlsanityData to 7 Oct 2026

Model-independent cosmological inference post DESI DR1 BAO measurements

arXiv 2405.19178 ↗First posted 29 May 2024astro-ph.CO cs.LG gr-qccited by 1

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Abstract (arXiv)

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 LambdaCDM model is always included in the 95% confidence intervals. We find evidence that the DESI LRG data at zeff = 0.51 is not an outlier within our model-independent framework. We study the Om-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 z. The entire exercise is later complemented by considering two more SN-Ia compilations - DES-5YR and Union3 - in combination with DESI BAO. Reconstruction with the DESI BAO + DES-5YR SN data sets predicts that the LambdaCDM model lies outside the 3σ confidence levels, whereas with DESI BAO + Union3 data, the LambdaCDM model is always included within 1σ. We also report constraints on H0 rd from our model-agnostic analysis, independent of the pre-recombination physics. Our results point towards an ≈ 2σ discrepancy between the DESI + Pantheon-Plus and DESI + DES-5YR data sets, which calls for further investigation.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 4 of its 96 references that are in the map. First posted on arXiv 29 May 2024.

Builds on

4 of 96 references.

References · 4

  1. 2022Neural Networks Optimized by Genetic Algorithms in Cosmologycited by 4
  2. 2023Neural network reconstruction of cosmology using the Pantheon compilationcited by 4
  3. 2024LADDER: Revisiting the Cosmic Distance Ladder with Deep Learning Approaches and Exploring its Applicationscited by 4
  4. 2024A possible late-time transition of MB inferred via neural networkscited by 2

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