Data-driven predictive modeling of Hubble parameter

We redesign the generalized pressure dark energy (GPDE) model, which is covering three common types of pressure parameterizations, with the help of a caloric framework to construct a theoretical ground for the machine learning (ML) analysis of cosmic Hubble parameter. The theoretical setup was optimized to find out appropriate values of its arbitrary parameters with the help of genetic neural network (GNN) algorithm and the most recent observational measurements of Hubble parameter. Since there is a shortcoming that the GNN process does not provide a direct method to calculate errors on the optimized values of free model parameters, we therefore take the Fisher Information Matrix (FIM) algorithm into account to deal with this issue. We see that the best-fitting value of Hubble constant and dimensionless dark energy density are in very good agreement with the most recent observations. Also, we discussed the optimized model from a cosmological perspective by making use of the evolutionary behavior of some cosmological parameters to present additional cosmological aspects of our theoretical proposal. It is concluded that our model implies physically meaningful results. In summary, the constructed model can explain the current accelerated expansion phase of the cosmos via Hubble parameter successfully.

Automated summary

In this study, we redesigned the generalized pressure dark energy model using a caloric framework and employed machine learning techniques to analyze the cosmic Hubble parameter. The optimized model parameters were obtained using a genetic neural network algorithm and the most recent observational measurements. Additionally, we addressed the issue of calculating errors on the optimized parameter values using the Fisher Information Matrix algorithm. The results showed good agreement with observational data and provided additional cosmological insights.