Can we learn from matter creation to solve the $H_{0}$ tension problem?

TL;DR

This paper explores matter creation as an alternative to dark energy to address the H0 tension, using Bayesian machine learning to constrain model parameters across different redshift ranges and identify potential solutions.

Contribution

It introduces a phenomenological matter creation model constrained by Bayesian machine learning, offering a novel approach to solving the H0 tension problem.

Findings

The 3αH0 term in the matter creation rate can potentially resolve the H0 tension.

Constraints on model parameters are successfully learned for multiple redshift ranges.

Forecasts suggest the model's viability at higher redshifts.

Abstract

The $H_{0}$ tension problem is studied in the light of a matter creation mechanism (an effective approach to replacing dark energy), the way to define the matter creation rate being of pure phenomenological nature. Bayesian (probabilistic) Machine Learning is used to learn the constraints on the free parameters of the models, with the learning being based on the generated expansion rate, $H(z)$. Taking advantage of the method, the constraints for three redshift ranges are learned. Namely, for the two redshift ranges: $z\in [0,2]$~(cosmic chronometers) and $z\in [0,2.5]$~(cosmic chronometers + BAO), covering already available $H(z)$ data, to validate the learned results; and for a third redshift interval, $z\in[0,5]$, for forecasting purposes. It is learned that the $3\alpha H_{0}$ term in the creation rate provides options that have the potential to solve the $H_{0}$ tension problem.