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Evolving Heterotic Gauge Backgrounds: Genetic Algorithms versus Reinforcement Learning

Abel, Steven; Constantin, Andrei; Harvey, Thomas R.; Lukas, Andre

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Authors

Andrei Constantin

Thomas R. Harvey

Andre Lukas



Abstract

The immensity of the string landscape and the difficulty of identifying solutions that match the observed features of particle physics have raised serious questions about the predictive power of string theory. Modern methods of optimisation and search can, however, significantly improve the prospects of constructing the standard model in string theory. In this paper we scrutinise a corner of the heterotic string landscape consisting of compactifications on Calabi-Yau three-folds with monad bundles and show that genetic algorithms can be successfully used to generate anomaly-free supersymmetric ๐‘†๐‘‚(10) GUTs with three families of fermions that have the right ingredients to accommodate the standard model. We compare this method with reinforcement learning and find that the two methods have similar efficacy but somewhat complementary characteristics.

Citation

Abel, S., Constantin, A., Harvey, T. R., & Lukas, A. (2022). Evolving Heterotic Gauge Backgrounds: Genetic Algorithms versus Reinforcement Learning. Fortschritte der Physik, 70(5), Article 2200034. https://doi.org/10.1002/prop.202200034

Journal Article Type Article
Online Publication Date Mar 16, 2022
Publication Date May 6, 2022
Deposit Date May 3, 2022
Publicly Available Date May 3, 2022
Journal Fortschritte der Physik
Print ISSN 0015-8208
Electronic ISSN 1521-3978
Publisher Wiley-VCH Verlag
Peer Reviewed Peer Reviewed
Volume 70
Issue 5
Article Number 2200034
DOI https://doi.org/10.1002/prop.202200034
Public URL https://durham-repository.worktribe.com/output/1208336

Files

Published Journal Article (Early view) (1.6 Mb)
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Publisher Licence URL
http://creativecommons.org/licenses/by/4.0/

Copyright Statement
Early view This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.






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