Analysis of Black Hole Solutions in Parabolic Class Using Neural Networks

TL;DR

This paper employs neural networks to analyze black hole solutions in high-dimensional parabolic classes, revealing the absence of self-similar critical solutions and challenging the universality of Choptuik phenomena.

Contribution

Introduces an ANN-based numerical method for analyzing black hole solutions in high-dimensional Einstein-axion-dilaton systems, providing new insights into their critical behavior.

Findings

No self-similar critical solutions found in high dimensions.

ANN estimators confirm absence of black hole solutions in the parabolic class.

Results question the universality of Choptuik phenomena in these systems.

Abstract

In this paper, we introduce a numerical method based on Artificial Neural Networks (ANNs) for the analysis of black hole solutions to the Einstein-axion-dilaton system in a high dimensional parabolic class. Leveraging a profile root-finding technique based on General Relativity we describe an ANN solver to directly tackle the system of ordinary differential equations. Through our extensive numerical analysis, we demonstrate, for the first time, that there is no self-similar critical solution for the parabolic class in the high dimensions of space-time. Specifically, we develop $95\%$ ANN-based confidence intervals for all the solutions in their domains. At the $95\%$ confidence level, our ANN estimators confirm that there is no black hole solution in higher dimensions, hence the gravitational collapse does not occur. Results provide some doubts about the universality of the Choptuik phenomena. Therefore, we conclude that the fastest-growing mode of the perturbations that determine the critical exponent does not exist for the parabolic class in the high dimensions.