神经网络方法在核质量中的应用  

Application of Neural Network Approach in Nuclear Mass

作　　者：李佳星 赵天亮 马娜娜 明星宸 张鸿飞 LI Jiaxing;ZHAO Tianliang;MA Nana;MING Xingchen;ZHANG Hongfei(School of Physics,Xi’an Jiaotong University,Xi’an 710049,China;School of Nuclear Science and Technology,Lanzhou University,Lanzhou 730000,China)

机构地区：[1]西安交通大学物理学院,陕西西安710049 [2]兰州大学核科学与技术学院,甘肃兰州730000

出　　处：《原子能科学技术》2023年第4期696-703,共8页Atomic Energy Science and Technology

基　　金：国家自然科学基金(12175170,11675066)。

摘　　要：基于两个具有代表性的宏观微观模型——有限程液滴模型(FRDM)和Weizs?cker-Skyrme模型(WS4),本文利用人工神经网络方法对模型所给出的数据进行了优化。加入神经网络方法后,FRDM所给出的结合能数据与2095个实验数据之间的均方根偏差从0.579 MeV降到0.354 MeV,WS4所给出的结合能数据与2095个实验数据之间的均方根偏差从0.292 MeV降到0.210 MeV。本文基于优化后的数据计算了Z=82同位素链的单中子分离能,FRDM和WS4单中子分离能的均方根偏差分别为39.9 keV和40.8 keV。此外,本文结合原始模型所给出的结合能数据,利用神经网络方法将数据进行了外推,FRDM和WS4在超重核区单中子分离能的均方根偏差分别为40.1 keV和188.1 keV。本文预测了新元素Z=119和Z=120同位素链的中子分离能,结果可为新元素合成的理论研究提供数据参考。The artificial neural network is a massively parallel distribution information processing system consisting of simple processing units.It has the ability to learn and build nonlinear complex relational models.Once trained by data set samples,it can predict the output.In recent years,it has been used in various extrapolations of nuclear physics and solved many complex problems.All the results point to the fact that the neural network approach is a very useful tool to further improve the accuracy of nuclea mass models,and can predict those unknown but critical nuclear data.Based on two representative macroscopic microscopic mass models:the finite-range droplet model(FRDM)and the Weizs cker-Skyrme model(WS4),the results of the model were optimized using the artificial neural network approach.After adding the neural network approach,the root mean square deviations of the binding energy of the two models are improved in varying degrees.The root mean square deviation between the binding energy data given by FRDM and 2095 experimental data decreases from 0.579 MeV to 0.354 MeV,and the root mean square deviation between the binding energy data given by WS4 and 2095 experimental data decreases from 0.292 MeV to 0.210 MeV.The one-neutron separation energy can reflect the accuracy of the nuclear mass model to a certain extent.Thus,based on the optimized data,the one-neutron separation energy of Z=82 isotope chain was calculated.The root mean square deviations of the one-neutron separation energy of FRDM and WS4 are 39.9 keV and 40.8 keV,respectively.In addition,accurate binding energy data of superheavy nuclei is very important for studying the synthesis of superheavy nuclei.In recent years,to find the exact position of the island of stability and extend the periodic table,many theoretical models for studying the synthesizing mechanism of superheavy nuclei are being developed.To accurately reproduce the experimental data of superheavy nuclear synthesis and reasonably predict the evaporation residual cross section of unknown

关 键 词：神经网络方法 质量模型 分离能 结合能 超重核 

分 类 号：O571[理学—粒子物理与原子核物理]