基于逐项模型差异的可解释混合核质量预测

中文摘要 AI 辅助

各种理论质量模型在再现实验质量方面一直取得了令人印象深刻的精度。然而，它们在未测量的丰中子区域的预测表现出明显的模型依赖性。在本研究中，我们通过比较两个代表性模型的液滴质量项，系统地研究了模型预测的差异。以两种广泛使用的模型，Weizsacker-Skyrme型（WS4）和Duflo-Zuker型（DZ10）作为代表例子，我们发现同位素链中的差异随着中子数的增加逐渐变得更加显著，不仅对于总结合能，而且对于各个质量项也是如此。最显著的差异出现在体积对称能中。通过利用WS4和DZ10液滴能量分量之间的逐项差异，我们引入了一个机器学习门控网络，自适应地结合这两个模型以提高预测精度。这种条件混合模型实现了比任一单独模型更低的均方根偏差（rmsd），将整体rmsd从0.284 MeV（WS4）和0.560 MeV（DZ10）降低到0.232 MeV。未来，这种逐项比较策略可以扩展到基于密度泛函理论的模型。

英文摘要

Various theoretical mass models have consistently achieved impressive accuracy in reproducing experimental masses. However, their predictions in unmeasured neutron-rich regions exhibit noticeable model dependence. In this study, we systematically investigate the differences in model predictions by comparing the liquid-drop mass terms of two representative models. Using two widely used models, Weizsacker-Skyrme-type (WS4) and Duflo-Zuker-type (DZ10), as representative examples, we find that the differences in an isotope chain gradually become more remarkable with increasing neutron number, not only for total binding energies but also for individual mass terms. The most noticeable difference appears in the volume-symmetry energy. By leveraging the term-by-term differences between the liquid-drop energy components of WS4 and DZ10, we introduce a machine learning gating network that adaptively combines the two models to improve predictive accuracy. This conditional hybrid model achieves a lower root-mean-square deviation (rmsd) than either model alone, reducing the overall rmsd from 0.284 MeV (WS4) and 0.560 MeV (DZ10) to 0.232 MeV. In the future, this term-by-term comparison strategy can be extended to models based on density-functional theories.

发表机构

• School of Physics and Optoelectronics, South China University of Technology（华南理工大学物理与光电工程学院）

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