An approach to cold dark matter deviation and the H₀ tension problem by using machine learning

2021/04/02 by E. Elizalde, Emilio Elizalde, Elizalde, Emilio +5 · 8 citations
Physics and Astronomy · #Astrophysics #Baryon #Cold dark matter #Cosmology #Cosmology and Gravitation Theories #Cosmology and Nongalactic Astrophysics (astro-ph.CO) #Dark energy #Dark matter #FOS: Physical sciences #Galaxies: Formation, Evolution, Phenomena #Galaxy #General Relativity and Quantum Cosmology (gr-qc) #Lambda-CDM model #Particle physics #Physics #Redshift #Scientific Research and Discoveries #astro-ph.CO #gr-qc

paper · pdf · doi:10.48550/arxiv.2104.01077

published in arXiv (Cornell University) (Cornell University) · 14 pages, 4 figures

arxiv created 2021/04/02 · openalex publication_date 2021/04/02 · arxiv updated 2021/04/05 · openalex created_date 2021/04/13 · openalex updated_date 2026/08/01

Abstract

In this work, two different models, one with cosmological constant Λ, and baryonic and dark matter (with ω_dm ≠ 0), and the other with an X dark energy (with ω_de ≠ -1), and baryonic and dark matter (with ω_dm ≠ 0), are investigated and compared. Using Bayesian machine learning analysis, constraints on the free parameters of both models are obtained for the three redshift ranges: z∈ [0,2], z∈ [0,2.5], and z∈ [0,5], respectively. For the first two redshift ranges, high-quality observations of the expansion rate H(z) exist already, and they are used for validating the fitting results. Additionally, the extended range z∈ [0,5] provides predictions of the model parameters, verified when reliable higher-redshift H(z) data are available. This learning procedure, based on the expansion rate data generated from the background dynamics of each model, shows that, at cosmological scales, there is a deviation from the cold dark matter paradigm, ω_dm ≠ 0, for all three redshift ranges. The results show that this approach may qualify as a solution to the H₀ tension problem. Indeed, it hints at how this issue could be effectively solved (or at least alleviated) in cosmological models with interacting dark energy.

Citations

• Dark energy cosmology: the equivalent description via different theoretical models and cosmography tests

Cited by

• Bayesian and Machine-Learning Analyses of Nonminimal f(Q) Gravity and H₀ Tension
• A Systematic Literature Review of Machine Learning Techniques for Observational Constraints in Cosmology
• Constraining deviations from ΛCDM in the Hubble expansion rate
• When Dark Matter Heats Up: A Model-Independent Search for Non-Cold Behavior

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