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...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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IOP Publishing
2024-01-01
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| Series: | Journal of Physics Communications |
| Subjects: | |
| Online Access: | https://doi.org/10.1088/2399-6528/ad246f |
| _version_ | 1797319215234940928 |
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| author | Enrique Escalante–Notario Ignacio Portillo–Castillo Saúl Ramos–Sánchez |
| author_facet | Enrique Escalante–Notario Ignacio Portillo–Castillo Saúl Ramos–Sánchez |
| author_sort | Enrique Escalante–Notario |
| collection | DOAJ |
| description | 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 geometries. Our neural network can be easily trained to successfully encode the large parameter space of many orbifold geometries simultaneously, independently of the statistical dissimilarities of their training features. In particular, we show that our autoencoder is capable of compressing with good accuracy the large parameter space of two promising orbifold geometries in just three parameters. Further, most orbifold models with phenomenologically appealing features appear in bounded regions of this small space. Our results hint towards a possible simplification of the classification of (promising) heterotic orbifold models. |
| first_indexed | 2024-03-08T04:03:36Z |
| format | Article |
| id | doaj.art-6b6b4ff8c5fd4189ac3d92150127eabd |
| institution | Directory Open Access Journal |
| issn | 2399-6528 |
| language | English |
| last_indexed | 2024-03-08T04:03:36Z |
| publishDate | 2024-01-01 |
| publisher | IOP Publishing |
| record_format | Article |
| series | Journal of Physics Communications |
| spelling | doaj.art-6b6b4ff8c5fd4189ac3d92150127eabd2024-02-09T09:35:03ZengIOP PublishingJournal of Physics Communications2399-65282024-01-018202500310.1088/2399-6528/ad246fAn autoencoder for heterotic orbifolds with arbitrary geometryEnrique Escalante–Notario0https://orcid.org/0000-0001-9748-853XIgnacio Portillo–Castillo1https://orcid.org/0000-0002-5807-9124Saúl Ramos–Sánchez2https://orcid.org/0000-0002-2643-2093Instituto de Física, Universidad Nacional Autónoma de México , POB 20-364, Cd.Mx. 01000, MexicoInstituto de Física, Universidad Nacional Autónoma de México , POB 20-364, Cd.Mx. 01000, Mexico; Facultad de Ingeniería Universidad Autónoma de Chihuahua, Nuevo Campus Universitario , Chihuahua 31125, MexicoInstituto de Física, Universidad Nacional Autónoma de México , POB 20-364, Cd.Mx. 01000, MexicoArtificial 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 geometries. Our neural network can be easily trained to successfully encode the large parameter space of many orbifold geometries simultaneously, independently of the statistical dissimilarities of their training features. In particular, we show that our autoencoder is capable of compressing with good accuracy the large parameter space of two promising orbifold geometries in just three parameters. Further, most orbifold models with phenomenologically appealing features appear in bounded regions of this small space. Our results hint towards a possible simplification of the classification of (promising) heterotic orbifold models.https://doi.org/10.1088/2399-6528/ad246fautoencoderorbifold modelsstring theory |
| spellingShingle | Enrique Escalante–Notario Ignacio Portillo–Castillo Saúl Ramos–Sánchez An autoencoder for heterotic orbifolds with arbitrary geometry Journal of Physics Communications autoencoder orbifold models string theory |
| title | An autoencoder for heterotic orbifolds with arbitrary geometry |
| title_full | An autoencoder for heterotic orbifolds with arbitrary geometry |
| title_fullStr | An autoencoder for heterotic orbifolds with arbitrary geometry |
| title_full_unstemmed | An autoencoder for heterotic orbifolds with arbitrary geometry |
| title_short | An autoencoder for heterotic orbifolds with arbitrary geometry |
| title_sort | autoencoder for heterotic orbifolds with arbitrary geometry |
| topic | autoencoder orbifold models string theory |
| url | https://doi.org/10.1088/2399-6528/ad246f |
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