Salti, Mehmet; Kangal, Evrim Ersin; Zengin, Bilgin

We turn our attention on evaluating the most recent Hubble parameter data measured via the differential evolution of cosmic-chronometers from a deep learning perspective. To achieve this goal, we start our investigation by introducing the selected theoretical setup and compiling the most recent statistical data obtained in cosmology experiments. Then we implement a tuned version of the long-short term memory (LSTM) architecture and run it to predict possible values of the Cosmic Hubble parameter for different red-shift states. Since we observe a good correlation between the observed and predicted datasets of the Hubble parameter, we conclude that the machine learning approaches can play important roles in the future cosmology investigations.

AtıfScopus2·WoS2·OpenAlex2

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Salti, Mehmet; Ciger, Emel; Kangal, Evrim Ersin; Zengin, Bilgin

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.

AtıfScopus7·WoS5

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