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原子核质量作为原子核的一个基本物理量之一, 对理解和研究原子核结构与核反应、核子核子基本相互作用等有重要作用, 但是精确预测远离 β 稳定线的原子核质量依旧是一个巨大挑战。本文基于机器学习优化的原子核质量表，研究了自 2022 年以来新测量的原子核质量、剩余质子-中子相互作用（δVpn）和重核 α 衰变能。研究表明：（1）对于 23 个新测量原子核，经机器学习优化后的质量表给出的均方根偏差在 0.51-0.58 MeV 之间，远低于液滴模型 (LDM)、 Weizsäcker-Skyrme-4 (WS4)、有限力程小液滴模型 (FRDM)、 Duflo–Zucker (DZ)质量表所给出的 3.275、 1.058、 0.752、 0.785 MeV。（2）机器学习优化后的质量表给出的 N=Z 时原子核的 δVpn与最新的实验数据相符合。（3）通过机器学习优化的原子核质量表计算得到的重核 α 衰变能的均方根偏差也大幅降低。此外，利用贝叶斯模型平均对四种机器学习优化后的质量模型进行加权平均，可以得到更精确的预测。这些结果表明，经过机器学习方法优化后的原子核质量表具有良好的外推能力，可以为相关研究提供有益的参考。本文数据集可在科学数据银行数据库https://doi.org/10.57760/sciencedb.j00213.00246中访问获取 (审稿阶段请通过私有访问链接查看本文数据集https://www.scidb.cn/s/iY3iQn)。
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关键词:
- 机器学习 /
- 原子核质量 /
- 剩余质子-中子相互作用 /
- α 衰变能
The mass of the atomic nucleus, as one of the fundamental physical quantities of the atomic nucleus, plays an important role in understanding and researching the structure of the atomic nucleus and nuclear reactions, the basic interactions between nucleons. However, accurately predicting the mass of nuclei far from the β stability line remains a huge challenge. Based on the machine-learning-refined mass model, the newly measured atomic nucleus masses since 2022, the residual proton-neutron interaction (δVpn), and the α-decay energy of heavy nucleus are studied. It is found that: (1) For the 23 newly measured atomic nuclei, the root mean square deviations obtained by the machine-learning-refined mass models are between 0.51 and 0.58 MeV, which are significantly lower than the 3.275, 1.058, 0.752, and 0.785 MeV given by the liquid droplet model (LDM), Weizsäcker-Skyrme-4 (WS4), finite-range droplet model (FRDM), and Duflo-Zucker (DZ), respectively. (2) The δVpn of the atomic nucleus with N=Z obtained from machine-learning-refined mass models is consistent with the latest experimental data. (3) The root mean square deviations of the α-decay energy of heavy nuclei obtained from the machine-learning-refined mass models have also been significantly reduced. Furthermore, by using the Bayesian model average approach to consider the results of different machine-learning-refined mass models, a more accurate prediction can be obtained. These results demonstrate that the machine-learning-refined mass models possess good extrapolation capabilities and can provide useful insight for further researches. The datasets presented in this paper, including the Scientific Data Bank, are openly available at https://doi.org/10.57760/sciencedb.j00213.00246 (Please use the private access link https://www.scidb.cn /s/iY3iQn to access the dataset during the peer review process)-
Keywords:
- Machine Learning /
- atomic nucleus mass /
- residual proton-neutron interaction /
- α-decay energy
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