Uncovering Singularities in Feynman Integrals via Machine Learning
Fuente:
arXiv
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| Autores principales: | , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866918172108521472 |
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| author | Liu, Yuanche Xu, Yingxuan Zhang, Yang |
| author_facet | Liu, Yuanche Xu, Yingxuan Zhang, Yang |
| contents | We introduce a machine-learning framework based on symbolic regression to extract the full symbol alphabet of multi-loop Feynman integrals. By targeting the analytic structure rather than reduction, the method is broadly applicable and interpretable across different families of integrals. It successfully reconstructs complete symbol alphabets in nontrivial examples, demonstrating both robustness and generality. Beyond accelerating computations case by case, it uncovers the analytic structure universally. This framework opens new avenues for multi-loop amplitude analysis and provides a versatile tool for exploring scattering amplitudes. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_10099 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Uncovering Singularities in Feynman Integrals via Machine Learning Liu, Yuanche Xu, Yingxuan Zhang, Yang High Energy Physics - Phenomenology Artificial Intelligence Machine Learning High Energy Physics - Theory We introduce a machine-learning framework based on symbolic regression to extract the full symbol alphabet of multi-loop Feynman integrals. By targeting the analytic structure rather than reduction, the method is broadly applicable and interpretable across different families of integrals. It successfully reconstructs complete symbol alphabets in nontrivial examples, demonstrating both robustness and generality. Beyond accelerating computations case by case, it uncovers the analytic structure universally. This framework opens new avenues for multi-loop amplitude analysis and provides a versatile tool for exploring scattering amplitudes. |
| title | Uncovering Singularities in Feynman Integrals via Machine Learning |
| topic | High Energy Physics - Phenomenology Artificial Intelligence Machine Learning High Energy Physics - Theory |
| url | https://arxiv.org/abs/2510.10099 |