Finite-volume extrapolation is an important step for extracting physical observables from lattice calculations.However,it is a significant challenge for systems with long-range interactions.We employ symbolic regressi...Finite-volume extrapolation is an important step for extracting physical observables from lattice calculations.However,it is a significant challenge for systems with long-range interactions.We employ symbolic regression to regress the 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.展开更多
基金supported in part by the National Natural Science Foundation of China(Grant Nos.12375072,12375073,12275259,and 12135011)supported by Guangdong Provincial Funding(Grant No.2019QN01X172)supported by the National Security Academic Fund(Grant No.U2330401)。
文摘Finite-volume extrapolation is an important step for extracting physical observables from lattice calculations.However,it is a significant challenge for systems with long-range interactions.We employ symbolic regression to regress the 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.
