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Hauptverfasser: Hatefi, Armin, Hatefi, Ehsan, Lopez-Sastre, Roberto J.
Format: Preprint
Veröffentlicht: 2024
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Online-Zugang:https://arxiv.org/abs/2406.04310
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author Hatefi, Armin
Hatefi, Ehsan
Lopez-Sastre, Roberto J.
author_facet Hatefi, Armin
Hatefi, Ehsan
Lopez-Sastre, Roberto J.
contents It is well-known that the critical gravitational collapse produces continuous self-similar solutions characterized by the Choptuik critical exponent, $γ$. We examine the solutions in the domains of the linear perturbation equations, considering the numerical measurement errors. Specifically, we study quantum perturbation theory for the four-dimensional Einstein-axion-dilaton system of the elliptic class of $\text{SL}(2,\mathbb{R})$ transformations. We develop a novel artificial neural network-assisted Metropolis-Hastings algorithm based on quantum perturbation theory to find the distribution of the critical exponent in a Bayesian framework. Unlike existing methods, this new probabilistic approach identifies the available deterministic solution and explores the range of physically distinguishable critical exponents that may arise due to numerical measurement errors.
format Preprint
id arxiv_https___arxiv_org_abs_2406_04310
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle Neural Networks Assisted Metropolis-Hastings for Bayesian Estimation of Critical Exponent on Elliptic Black Hole Solution in 4D Using Quantum Perturbation Theory
Hatefi, Armin
Hatefi, Ehsan
Lopez-Sastre, Roberto J.
High Energy Physics - Theory
General Relativity and Quantum Cosmology
Mathematical Physics
Quantum Physics
It is well-known that the critical gravitational collapse produces continuous self-similar solutions characterized by the Choptuik critical exponent, $γ$. We examine the solutions in the domains of the linear perturbation equations, considering the numerical measurement errors. Specifically, we study quantum perturbation theory for the four-dimensional Einstein-axion-dilaton system of the elliptic class of $\text{SL}(2,\mathbb{R})$ transformations. We develop a novel artificial neural network-assisted Metropolis-Hastings algorithm based on quantum perturbation theory to find the distribution of the critical exponent in a Bayesian framework. Unlike existing methods, this new probabilistic approach identifies the available deterministic solution and explores the range of physically distinguishable critical exponents that may arise due to numerical measurement errors.
title Neural Networks Assisted Metropolis-Hastings for Bayesian Estimation of Critical Exponent on Elliptic Black Hole Solution in 4D Using Quantum Perturbation Theory
topic High Energy Physics - Theory
General Relativity and Quantum Cosmology
Mathematical Physics
Quantum Physics
url https://arxiv.org/abs/2406.04310