2024/01/19 by Martiros Khurshudyan, E. Elizalde, Khurshudyan, Martiros +1 · 2 citations
Earth and Planetary Sciences · Physics and Astronomy · #Astronomy and Astrophysical Research #Cosmology and Gravitation Theories #Cosmology and Nongalactic Astrophysics (astro-ph.CO) #FOS: Physical sciences #General Relativity and Quantum Cosmology (gr-qc) #Geophysics and Gravity Measurements #High Energy Physics - Phenomenology (hep-ph) #High Energy Physics - Theory (hep-th)
paper · pdf · doi:10.48550/arxiv.2402.08630
openalex publication_date 2024/01/19 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Recently, using Bayesian Machine Learning, a deviation from the cold dark matter model on cosmological scales has been put forward. Such model might replace a proposed non-gravitational interaction between dark energy and dark matter, and help solve the H0 tension problem. The idea behind the learning procedure relied there on a generated expansion rate, while the real expansion rate was just used to validate the learned results. In the present work, however, the emphasis is put on a Gaussian Process (GP) with the available H(z) data confirming the possible existence of the already learned deviation. Three cosmological scenarios are considered: a simple one, with equation of state parameter for dark matter ωdm = ω0 ≠ 0, and two other models, with corresponding parameters ωdm = ω0 + ω1 z and ωdm = ω0 + ω1 z/(1+z). The constraints obtained on the free parameters ω0 and ω1 hint towards a dynamical nature of the deviation. The dark energy dynamics is also reconstructed, revealing interesting aspects connected with the H0 tension problem. It is concluded, however, that improved tools and more data are needed, in order to reach a better understanding of the reported deviation.