AI 中文总结
研究基于机器学习优化质量模型，对2022年以来新测原子核质量、剩余质子 - 中子相互作用及重核α衰变能量进行研究，通过对比多种模型偏差及结合贝叶斯模型平均方法，展现模型良好外推能力，为相关研究提供见解。
AI 中文摘要
原子核质量作为原子核的基本物理量之一，在理解和研究原子核结构、核反应以及核子间基本相互作用中起着重要作用。然而，准确预测远离β稳定线的原子核质量仍是巨大挑战。基于机器学习优化的质量模型，研究了2022年以来新测量的原子核质量、剩余质子 - 中子相互作用（δVpn）和重核的α衰变能量。发现：对于23个新测量的原子核，机器学习优化质量模型得到的均方根偏差在0.51至0.58MeV之间，显著低于液滴模型（LDM）、魏茨泽克 - 斯凯姆 - 4（WS4）、有限范围液滴模型（FRDM）和杜弗洛 - 祖克（DZ）给出的偏差。从机器学习优化质量模型得到的N = Z原子核的δVpn与最新实验数据一致，重核α衰变能量的均方根偏差也显著降低。此外，采用贝叶斯模型平均方法结合不同机器学习优化质量模型的结果，得到了更准确的预测。这些发现表明此类模型具有良好的外推能力，为进一步研究提供了有用的见解。本文给出的数据集可在指定网址公开获取。
英文摘要
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, and 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, we investigate the newly measured atomic nucleus masses since 2022, along with the residual proton-neutron interaction (δVpn) and the α-decay energy of heavy nucleus. 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 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 employing the Bayesian model average approach to combine the results from different machine-learning-refined mass models, we obtain more accurate predictions. These findings demonstrate that such models have good extrapolation capabilities and provide useful insight for further research. The datasets presented in this paper are openly available at https://doi.org/10.57760/sciencedb.j00213.00246.
