An analysis of the $H_{0}$ tension problem in a universe with a viscous dark fluid

TL;DR

This paper introduces two inhomogeneous single fluid models for the universe that address the H0 tension problem, utilizing a Bayesian machine learning approach to constrain model parameters across different redshift ranges and validate against observational data.

Contribution

The paper presents a novel Bayesian machine learning method to constrain inhomogeneous dark fluid models for the universe, capable of explaining high-redshift H(z) data and addressing the H0 tension.

Findings

Models fit well with mock and real H(z) data.

Second model explains BOSS H(z) at z=2.34.

Method is fully falsifiable with future data.

Abstract

In this paper, two inhomogeneous single fluid models for the Universe, which are able to naturally solve the $H_{0}$ tension problem, are discussed. The analysis is based on a Bayesian Machine Learning approach that uses a generative process. The adopted method allows to constrain the free parameters of each model by using the model itself, only. The observable is taken to be the Hubble parameter, obtained from the generative process. Using the full advantages of our method, the models are constrained for two redshift ranges. Namely, first this is done with mock $H(z)$ data over $z\in [0,2.5]$, thus covering known $H(z)$ observational data, which are most helpful to validate the fit results. Then, aiming to extend to redshift ranges to be covered by the most recent ongoing and future planned missions, the models are constrained for the range $z\in[0,5]$, too. Full validation of the results for this extended redshift range will have to wait for the near future, when higher redshift $H(z)$ data become available. This makes our models fully falsifiable. Finally, our second model here is able to explain the BOSS reported value for $H(z)$ at $z=2.34$.