Constraints on Prospective Deviations from the Cold Dark Matter Model Using a Gaussian Process

Abstract

Recently, using Bayesian Machine Learning, a deviation from the cold dark matter model on cosmological scales has been put forward. Such a model might replace the proposed non-gravitational interaction between dark energy and dark matter, and help solve the H0 tension problem. The idea behind the learning procedure relies on a generated expansion rate, while the real expansion rate is 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 an equation-of-state parameter for dark matter ωdm=ω0≠0, and two other models, with corresponding parameters ωdm=ω0+ω1z and ωdm=ω0+ω1z/(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, to reach a better understanding of the reported deviation.