Constraints on prospective deviations from the cold dark matter model using a Gaussian Process

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 $H_{0}$ 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 $\omega_{dm} = \omega_{0} \neq 0$, and two other models, with corresponding parameters $\omega_{dm} = \omega_{0} + \omega_{1} z$ and $\omega_{dm} = \omega_{0} + \omega_{1} z/(1+z)$. The constraints obtained on the free parameters $\omega_{0}$ and $\omega_{1}$ hint towards a dynamical nature of the deviation. The dark energy dynamics is also reconstructed, revealing interesting aspects connected with the $H_{0}$ 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.