Machine Learning Unveils the Power Law of Finite-Volume Energy Shifts
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arXiv
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| Auteurs principaux: | , , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866911080790360064 |
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| author | Zhang, Wei-Jie Zhang, Zhenyu Hu, Jifeng Lu, Bing-Nan Pang, Jin-Yi Wang, Qian |
| author_facet | Zhang, Wei-Jie Zhang, Zhenyu Hu, Jifeng Lu, Bing-Nan Pang, Jin-Yi Wang, Qian |
| contents | Finite-volume extrapolation is an important step for extracting physical observables from lattice calculations. However, it is a significant challenge for the system with long-range interactions. We employ symbolic regression to regress finite-volume extrapolation formula for both short-range and long-range interactions. The regressed formula still holds the exponential form with a factor $L^n$ in front of it. The power decreases with the decreasing range of the force. When the range of the force becomes sufficiently small, the power converges to $-1$, recovering the short-range formula as expected. Our work represents a significant advancement in leveraging machine learning to probe uncharted territories within particle physics. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_06496 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Machine Learning Unveils the Power Law of Finite-Volume Energy Shifts Zhang, Wei-Jie Zhang, Zhenyu Hu, Jifeng Lu, Bing-Nan Pang, Jin-Yi Wang, Qian High Energy Physics - Phenomenology High Energy Physics - Lattice Finite-volume extrapolation is an important step for extracting physical observables from lattice calculations. However, it is a significant challenge for the system with long-range interactions. We employ symbolic regression to regress finite-volume extrapolation formula for both short-range and long-range interactions. The regressed formula still holds the exponential form with a factor $L^n$ in front of it. The power decreases with the decreasing range of the force. When the range of the force becomes sufficiently small, the power converges to $-1$, recovering the short-range formula as expected. Our work represents a significant advancement in leveraging machine learning to probe uncharted territories within particle physics. |
| title | Machine Learning Unveils the Power Law of Finite-Volume Energy Shifts |
| topic | High Energy Physics - Phenomenology High Energy Physics - Lattice |
| url | https://arxiv.org/abs/2503.06496 |