Stochastic analysis of finite-temperature effects on cosmological parameters by artificial neural networks
Armin Hatefi, Ehsan Hatefi, and I. Y. Park

TL;DR
This study investigates how finite-temperature quantum gravity effects influence cosmological parameters, especially the cosmological constant, by integrating temperature-dependent quantum corrections into cosmological models and analyzing their impact on observational data using neural networks.
Contribution
It introduces new density parameters from finite-temperature quantum gravity effects into cosmological models and demonstrates their potential to improve fit to observational data using machine learning techniques.
Findings
Negative value of $\Omega_{\Lambda_2}$ consistent with dimensional regularization
Enhanced model accuracy with additional quantum gravity parameters
Improved fit to Planck 2018 data with these parameters
Abstract
We explore the impact of finite-temperature quantum gravity effects on cosmological parameters, particularly the cosmological constant , by incorporating temperature-dependent quantum corrections into the Hubble parameter. For that purpose, we modify the Cosmic Linear Anisotropy Solving System. We introduce new density parameters, and , arising from finite-temperature quantum gravity contributions, and analyze their influence on the cosmic microwave background power spectrum using advanced machine learning techniques, including artificial neural networks and stochastic optimization. Our results reveal that assumes a negative value, consistent with dimensional regularization in renormalization and that the presence of as well as enhances model accuracy. Numerical analyses…
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Taxonomy
TopicsStatistical and numerical algorithms · Material Science and Thermodynamics
