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| Autori principali: | , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2512.10907 |
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| _version_ | 1866918244701437952 |
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| author | Mishra, Challenger Tan, Justin |
| author_facet | Mishra, Challenger Tan, Justin |
| contents | We compute solutions to the Hermitian Yang-Mills equations on holomorphic vector bundles $V$ via an alternating optimisation procedure founded on geometric machine learning. The proposed method is fully general with respect to the rank and structure group of $V$, requiring only the ability to enumerate a basis of global sections for a given bundle. This enables us to compute the physically normalised Yukawa couplings in a broad class of heterotic string compactifications. Using this method, we carry out this computation in full for a heterotic compactification incorporating a gauge bundle with non-Abelian structure group. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_10907 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Hermitian Yang--Mills connections on general vector bundles: geometry and physical Yukawa couplings Mishra, Challenger Tan, Justin High Energy Physics - Theory Machine Learning We compute solutions to the Hermitian Yang-Mills equations on holomorphic vector bundles $V$ via an alternating optimisation procedure founded on geometric machine learning. The proposed method is fully general with respect to the rank and structure group of $V$, requiring only the ability to enumerate a basis of global sections for a given bundle. This enables us to compute the physically normalised Yukawa couplings in a broad class of heterotic string compactifications. Using this method, we carry out this computation in full for a heterotic compactification incorporating a gauge bundle with non-Abelian structure group. |
| title | Hermitian Yang--Mills connections on general vector bundles: geometry and physical Yukawa couplings |
| topic | High Energy Physics - Theory Machine Learning |
| url | https://arxiv.org/abs/2512.10907 |