摘要
近年来,机器学习方法被广泛应用于对原子核质量的预测中。基于连续型贝叶斯概率(Continuous Bayesian Probability,CBP)估计器,结合贝叶斯模型平均(Bayesian Model Averaging,BMA)改进了理论模型对核质量的描述。在CBP方法中,核质量理论值与实验值的差异被视为连续变量,通过核密度估计(Kernel Density Estimation,KDE)生成其先验和似然概率密度函数,并以贝叶斯定理确定后验概率密度函数。在全局优化和外推分析中,CBP方法显著提升了理论模型预测的精确度。此外,BMA方法基于各模型对基准核的预测表现为每个模型分配权重,平衡了各模型在不同区域的优势。通过预测Ca同位素双中子滴线位置,评估了BMA方法修正结果的可靠性。CBP方法结合BMA方法为预测未知区域的核质量提供了一种有效途径,并可应用于对其他核性质的研究。
[Background]In recent years,machine learning methods have been widely applied to the predictions of nuclear masses.[Purpose]This study aims to employ the continuous Bayesian probability(CBP)estimator and the Bayesian model averaging(BMA)to optimize the descriptions of sophisticated nuclear mass models.[Methods]The CBP estimator treated the residual between the theoretical and experimental values of nuclear masses as a continuous variable,deriving its posterior probability density function(PDF)from Bayesian theory.The BMA method assigned weights to models based on their predictive performance for benchmark nuclei,thereby balancing each model's unique strengths.[Results]In global optimization,the CBP method improves the Hartree-Fock-Bogoliubov(HFB)model by approximately 90%,and the relativistic mean-filed(RMF)and semi-empirical formulas by 70%and 50%,respectively.In extrapolation analysis,the CBP method improves the prediction accuracy for the HFB models,RMF models,and semi-empirical formulas by approximately 80%,55%,and 50%,respectively,demonstrating the strong generalization ability of the CBP method.To assess the reliability of the BMA method,the two-neutron separation energy for Ca isotopes was extrapolated,and its two-neutron drip line was predicted.[Conclusions]The methods proposed in this paper provide an effective way to accurately predict the nuclear mass,with potential applications to research on other nuclear properties.
作者
谭凯中
高琬晴
刘健
TAN Kaizhong;GAO Wanqing;LIU Jian(College of Science,China University of Petroleum(East China),Qingdao 266580,China)
出处
《核技术》
北大核心
2025年第5期95-105,共11页
Nuclear Techniques
基金
国家自然科学基金面上项目(No.12475135)资助。
关键词
机器学习
核质量
连续型贝叶斯估计器
贝叶斯模型平均
Machine learning
Nuclear masses
Continuous Bayesian probability estimator
Bayesian model averaging
