Explainable AI-assisted Optimization for Feynman Integral Reduction
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arXiv
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| Auteurs principaux: | , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866909492124319744 |
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| author | Song, Zhuo-Yang Yang, Tong-Zhi Cao, Qing-Hong Luo, Ming-xing Zhu, Hua Xing |
| author_facet | Song, Zhuo-Yang Yang, Tong-Zhi Cao, Qing-Hong Luo, Ming-xing Zhu, Hua Xing |
| contents | We present a novel approach to optimizing the reduction of Feynman integrals using integration-by-parts identities. By developing a priority function through the FunSearch algorithm, which combines large language models and genetic algorithms, we achieve significant improvements in memory usage and computational efficiency compared to traditional methods. Our approach demonstrates substantial reductions in the required seeding integrals, making previously intractable integrals more manageable. Tested on a variety of Feynman integrals, including one-loop and multi-loop cases with planar and non-planar configurations, our method demonstrates remarkable scalability and adaptability. For reductions of certain Feynman integrals with many dots and numerators, we observed an improvement by a factor of 3058 compared to traditional methods. This work provides a powerful and interpretable framework for optimizing IBP reductions, paving the way for more efficient and practical calculations in high-energy physics. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_09544 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Explainable AI-assisted Optimization for Feynman Integral Reduction Song, Zhuo-Yang Yang, Tong-Zhi Cao, Qing-Hong Luo, Ming-xing Zhu, Hua Xing High Energy Physics - Phenomenology High Energy Physics - Theory We present a novel approach to optimizing the reduction of Feynman integrals using integration-by-parts identities. By developing a priority function through the FunSearch algorithm, which combines large language models and genetic algorithms, we achieve significant improvements in memory usage and computational efficiency compared to traditional methods. Our approach demonstrates substantial reductions in the required seeding integrals, making previously intractable integrals more manageable. Tested on a variety of Feynman integrals, including one-loop and multi-loop cases with planar and non-planar configurations, our method demonstrates remarkable scalability and adaptability. For reductions of certain Feynman integrals with many dots and numerators, we observed an improvement by a factor of 3058 compared to traditional methods. This work provides a powerful and interpretable framework for optimizing IBP reductions, paving the way for more efficient and practical calculations in high-energy physics. |
| title | Explainable AI-assisted Optimization for Feynman Integral Reduction |
| topic | High Energy Physics - Phenomenology High Energy Physics - Theory |
| url | https://arxiv.org/abs/2502.09544 |