Relativity and Cosmology


Machine Learning Algorithm in a Caloric View Point of Cosmology

Authors: E. E. Kangal, M. Saltiy, O. Aydogdu

In the present work, we mainly discuss the variable polytropic gas (VPG henceforth) proposal, which is describing a self-gravitating gaseous sphere and can be considered as a crude approximation for realistic stellar denitions, from a caloric perspective. In order to reach this aim, we start with reconstructing the VPG model by making use of thermodynamics. And then, the auxiliary parameters written in the proposal are tted by focusing on updated experimental dataset published in literature. We also discuss the model in view of the statistical perspective and conclude that the caloric VPG model (cVPG henceforth) is in good agreement with the recent astrophysical observations. With the help of the statistical discussions, we see that the cVPG model is suitable for the statistical cosmology and can be used to make useful predictions for the future of the universe via the machine learning (ML henceforth) methods like the linear regression (LR henceforth) algorithm. Moreover, according to the results, we also perform a rough estimation for the lifetime of the universe and conclude that the cosmos will be torn apart after 51Gyr which means our universe has spent 21 percent of its lifetime.

Comments: 20 Pages. Physics of the Dark Universe 26 (2019) 100369

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Submission history

[v1] 2019-10-17 04:37:45

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