An autoencoder for heterotic orbifolds with arbitrary geometry

An autoencoder for heterotic orbifolds with arbitrary geometry

Artificial neural networks can be an important tool to improve the search for admissible string compactifications and characterize them. In this paper we construct the heterotic orbiencoder , a general deep autoencoder to study heterotic orbifold models arising from various Abelian orbifold geometri...

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Bibliographic Details
Main Authors: Enrique Escalante–Notario, Ignacio Portillo–Castillo, Saúl Ramos–Sánchez
Format: Article
Language:English
Published: IOP Publishing 2024-01-01
Series:Journal of Physics Communications
Subjects:
autoencoder
orbifold models
string theory
Online Access:https://doi.org/10.1088/2399-6528/ad246f

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