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arXiv · 2503.06496

Machine Learning Unveils the Power Law of Finite-Volume Energy Shifts

Wei-Jie Zhang·Zhenyu Zhang·Jifeng Hu·Bing-Nan Lu·Jin-Yi Pang·Qian Wang
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Abstract

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.

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Wei-Jie Zhang, Zhenyu Zhang, Jifeng Hu, Bing-Nan Lu, Jin-Yi Pang, Qian Wang. 2025-03-09. Machine Learning Unveils the Power Law of Finite-Volume Energy Shifts. https://doi.org/10.1088/0256-307x%2F42%2F7%2F070202

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