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

Understand

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.