Autoencoder-driven clustering of intersecting D-brane models via tadpole charge
Abstract We study the well-known type IIA intersecting D-brane models on the T 6 / ℤ 2 × ℤ 2 ′ $$ {T}^6/\left({\mathbb{Z}}_2\times {\mathbb{Z}}_2^{\prime}\right) $$ orientifold via a machine-learning approach. We apply several autoencoder models with and without positional encoding to D6-brane confi...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
SpringerOpen
2024-08-01
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| Series: | Journal of High Energy Physics |
| Subjects: | |
| Online Access: | https://doi.org/10.1007/JHEP08(2024)133 |
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| Summary: | Abstract We study the well-known type IIA intersecting D-brane models on the T 6 / ℤ 2 × ℤ 2 ′ $$ {T}^6/\left({\mathbb{Z}}_2\times {\mathbb{Z}}_2^{\prime}\right) $$ orientifold via a machine-learning approach. We apply several autoencoder models with and without positional encoding to D6-brane configurations satisfying certain concrete models described in ref. [1] and attempt to extract some features which the configurations possess. We observe that the configurations cluster in two-dimensional latent layers of the autoencoder models and analyze which physical quantities are relevant to the clustering. As a result, it is found that tadpole charges of hidden D6-branes characterize the clustering. We expect that there is another important factor because a checkerboard pattern in two-dimensional latent layers is observed in the clustering. |
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| ISSN: | 1029-8479 |