利用机器学习方法对几个核物理问题的深入研究

Studies on several problems in nuclear physics by using machine learning

机器学习能够从大量复杂的数据中挖掘其内在的关联,近些年被广泛应用在科学研究中。本文分别评估了两类机器学习算法在修正核质量、重构重离子反应碰撞参数以及提取对称能斜率系数等热点问题上的表现,并讨论了机器学习模型的外推能力和泛化能力。结果表明:机器学习方法在三个核物理问题的研究中均体现出强大的能力。将机器学习方法应用于核物理问题的研究中可以进一步探索新物理,从而更好地推动理论和实验的发展。

[Background]Machine learning,which has been widely applied to scientific research in recent years,can be used to investigate the inherent correlations within a large number of complex data.[Purpose]We evaluate the performances of two types of machine-learning algorithms for correcting nuclear mass models,reconstructing the impact parameter in heavy-ion collisions,and extracting the symmetry energy slope parameter.We also discuss the extrapolation and generalization ability of the machine-learning models.[Method]For correcting the nuclear mass models,10 characteristic quantities are fed into the LightGBM to mimic the residual between the experimental and the theoretical binding energies.For impact parameter or symmetry energy,two types of observables constructed based on the particle information simulated by using the UrQMD transport model for setting up the different impact parameters or symmetry energy slope parameters are used as inputs to a conventional neural network and the LightGBM to extract the original information.[Result]Analysis of these nuclear physics problems reveals the potential applicability of machine-learning methods.[Conclusions]Machine-learning methods can be used to investigate new physical problems,thereby promoting the development of both theory and experiment.