摘要
慢中子俘获过程(s-过程)产生了自然界中约一半比Fe重的元素,中子俘获率是s-过程研究的关键核物理输入量。近年来,机器学习方法在核物理研究中的应用取得了很大的成功,其中包括了机器学习核脊回归方法对中子俘获率的研究。为了探究机器学习方法对中子俘获率的修正对s-过程研究的影响,本文分别利用修正前后的中子俘获率数据,基于核反应网络进行了s-过程模拟,并对所得s-过程丰度进行了对比分析。结果表明,机器学习对中子俘获率的修正对s-过程模拟所得的丰度分布整体影响较小,但对处于s-过程路径上的个别重要核素的丰度影响可达30%左右。
The origin of elements in the universe is a basic scientific problem.The slow neutron capture process(s-process)is believed to be responsible for the nucleosynthesis of about half of the elements heavier than iron.The neutron capture reaction rates are crucial nuclear physics inputs for the s-process,as they can affect the reaction flow and the s-process branchings along the s-process path.Over the past decades,increasingly powerful computers and advances in machine-learning(ML)methods have driven explosive applications of ML in many fields of physics,including nuclear physics.Recently,the ML kernel ridge regression(KRR)approach have been successfully employed to improve the theoretical predictions of neutron capture reaction rates.The corresponding results indicate that the corrections might lead to positive influences on the s-process simulations.This work aims to investigate the detailed effects of the KRR corrections of the neutron capture reaction rates on the s-process simulations.The s-process simulations were performed with the nuclear reaction network calculations based on the NucNet tools.The neutron capture reaction rates were taken from the Talys calculations and the KRR predictions,respectively,when the experimental data in the KADoNiS database were not available.Other nuclear physics inputs,includingβdecay rates andαdecay rates,were taken from the JINA Reaclib database.The astrophysical conditions of the s-process simulations were taken as the typical values,i.e.[KG-*3],temperature kT=30 keV and neutron density n n=1.6×107 cm-3.The final s-process abundances were obtained by the superposition of the abundances from simulations with different irradiation time with the weights following an exponential distribution.It is found that the final s-process abundances from both simulations based on the Talys calculations and the KRR predictions can reasonably reproduce the Solar s-abundances.The abundances produced by both simulations are actually very similar,which means that the KRR corrections on the n
作者
黄天行
吴鑫辉
HUANG Tianxing;WU Xinhui(State Key Laboratory of Nuclear Physics and Technology,School of Physics,Peking University,Beijing 100871,China)
出处
《原子能科学技术》
EI
CAS
CSCD
北大核心
2023年第4期743-750,共8页
Atomic Energy Science and Technology
基金
国家重点研发计划(2018YFA0404400,2017YFE0116700)
国家自然科学基金(11875075,11935003,11975031,12141501,12070131001)
中国博士后科学基金(2021M700256)。
关键词
S-过程
中子俘获率
机器学习
核脊回归方法
s-process
neutron capture reaction rate
machine-learning
kernel ridge regression
