Evolutionary Algorithms for Multi‐Center Solutions

Large classes of multi‐center supergravity solutions have been constructed in the study of supersymmetric black holes and their microstates. Many smooth multi‐center solutions have the same charges as supersymmetric black holes, with all centers deep inside a long black‐hole‐like throat. These confi...

Full description

Saved in:
Bibliographic Details
Published inFortschritte der Physik Vol. 72; no. 2
Main Authors Rawash, Sami, Turton, David
Format Journal Article
LanguageEnglish
Published Weinheim Wiley Subscription Services, Inc 01.02.2024
Subjects
Online AccessGet full text
ISSN0015-8208
1521-3978
1521-3978
DOI10.1002/prop.202300255

Cover

More Information
Summary:Large classes of multi‐center supergravity solutions have been constructed in the study of supersymmetric black holes and their microstates. Many smooth multi‐center solutions have the same charges as supersymmetric black holes, with all centers deep inside a long black‐hole‐like throat. These configurations are constrained by regularity, absence of closed timelike curves, and charge quantization. Due to these constraints, constructing explicit solutions with several centers in generic arrangements, and with all parameters in physically relevant ranges, is a hard task. In this work, an optimization algorithm, based on evolutionary algorithms and Bayesian optimization is presented, that systematically constructs numerical solutions satisfying all constraints. Explicit examples of novel five‐center and seven‐center machine‐precision solutions are exhibited. Large classes of multi‐center supergravity solutions have been constructed in the study of supersymmetric black holes and their microstates. Many smooth multi‐center solutions have the same charges as supersymmetric black holes, with all centers deep inside a long black‐hole‐like throat. These configurations are constrained by regularity, absence of closed timelike curves, and charge quantization. Due to these constraints, constructing explicit solutions with several centers in generic arrangements, and with all parameters in physically relevant ranges, is a hard task. In this work, an optimization algorithm, based on evolutionary algorithms and Bayesian optimization is presented, that systematically constructs numerical solutions satisfying all constraints. Explicit examples of novel five‐center and seven‐center machine‐precision solutions are exhibited.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:0015-8208
1521-3978
1521-3978
DOI:10.1002/prop.202300255