摘要
原子核的β衰变寿命决定了天体快中子俘获核合成过程(r-过程)的时间标度,其精确描述对r-过程研究十分重要.本文利用机器学习方法,通过构建三种不同的神经网络,给出了整个核素图上原子核β衰变寿命的预测及其误差,研究了神经网络输入量、神经元个数和激活函数的选取对预测结果的影响.与基于有限程小液滴模型的无规相位近似理论(FRDM+QRPA)相比,对原子核β衰变寿命的描述精度提升了约2.6倍,与实验的均方根偏差达到了10^(0.43);对于寿命小于1 s的原子核,精度达到了10^(0.22)这将对r-过程模拟研究产生重要的影响.
Nuclearβ-decay half-lives determine the time scale of the rapid neutron capture process(r-process)in astrophysics;thus,their accurate description is crucial for the study of the r-process.In this work,we use the machine learning method to predict the nuclearβ-decay half-lives and their error bars for the whole nuclear chart by constructing three different neural networks.We study the influences on the results of different neural network inputs,number of neurons,and activation functions.Compared with the quasiparticle random-phase approximation based on the finite range droplet model(FRDM+QRPA),the accuracy of the description of half-lives is improved by about 2.6 times,and the root mean square(rms)deviation from experimental data reaches 10^(0.43).For the nuclei with half-lives of<1 s,the rms value reaches 10^(0.22),which can have an important effect on the r-process simulation study.
作者
李鹏
白景虎
牛中明
牛一斐
LI Peng;BAI JingHu;NIU ZhongMing;NIU YiFei(School of Nuclear Science and Technology,Lxinzhou University,Lanzhou 730000,China;School of Physics and Materials Science,Anhui University,Hefei 230000,China)
出处
《中国科学：物理学、力学、天文学》
CSCD
北大核心
2022年第5期53-59,共7页
Scientia Sinica Physica,Mechanica & Astronomica
基金
国家自然科学基金(编号:12075104)资助项目。
关键词
β衰变寿命
神经网络
机器学习
β-decay half-life
neural network
machine learning
