Analysis of the H0 tension problem in the Universe with viscous dark fluid

Abstract

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 method here adopted allows for constraint of the free parameters of each model by making use of 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 HðzÞ data over z ∈ ½0; 2.5, thus covering known HðzÞ 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 ∈ ½0; 5, too. Full validation of the results for this extended redshift range will have to wait for some years, for when higher-redshift HðzÞ data become available. This makes our models fully falsifiable. In addition, our second model is able to explain the BOSS reported value for HðzÞ at z ¼ 2.34.

Interplay between Swampland and Bayesian Machine Learning in constraining cosmological models

“…Then, during the Bayesian (Probabilistic) Learning process, the priors are updated through mock data generation and the Machine Learning algorithm. Recently, it has been successfully applied to study the H 0 tension problem, by using single viscous fluid models, and also to constrain the cosmic opacity of the Universe at different redshift ranges [47,48]. Having realized the power of the approach through these previous successes, in this paper we have taken a step forward, by incorporating into the analysis string Swampland criteria.…”

“…It means that it takes into account all possible uncertainties during generating and learning process. In other words, 8 We address the readers to [47,48] for more details about the difference between Machine Learning and Bayesian Machine Learning that rose namely in this step. We used 10 chains and in each chain generated/simulated 10000 "datasets" to cover z ∈ [0, 2.5] and z ∈ [0, 5] redshift ranges.…”

“…Moreover, using the best fit values of the parameters of the model, the top-right plot of Fig. 2 shows that the model 10 We refer to [46][47][48] for more details about what the Bayesian (Probabilistic) Machine Learning says about the H 0 tension problem. can also explain the BOSS result for the Hubble parameter, at z = 2.34.…”

“…Moreover, it should be mentioned that it is still not possible to do the same by means of Bayesian analysis, where the likelihood function has to be evaluated. What renders the analysis here now possible is the significant development in machine learning [46][47][48] (and references therein) occurred recently. In particular, when we consider the Bayesian (Probabilistic) Machine Learning approach that uses a model-based generative process, it becomes possible to perform the task of constraining the cosmological model by involving the string Swampland criteria presented above.…”

Late Time Attractors of Some Varying Chaplygin Gas Cosmological Models

“…Recently another one known as the H 0 tension problem has been added to this list [15][16][17][18]. The goal of this paper is (1) to consider various new cosmological models explaining the late time accelerated expansion of the universe [19][20][21][22][23][24][25][26], and (2) to see whether or not the models solve the H 0 tension problem [27][28][29][30][31][32][33][34][35][36][37]. The analysis of the models is based on two approaches.…”