Prediction of nuclear charge density distribution with feedback neural network  

作　　者：Tian‑Shuai Shang Jian Li Zhong‑Ming Niu 

机构地区：[1]College of Physics,Jilin University,Changchun 130012,China [2]School of Physics and Optoelectronic Engineering,Anhui University,Hefei 230601,China

出　　处：《Nuclear Science and Techniques》2022年第12期24-35,共12页核技术（英文）

基　　金：supported by the Natural Science Foundation of Jilin Province (No. 20220101017JC);the National Natural Science Foundation of China (Nos. 11675063, 11875070, and 11935001);Key Laboratory of Nuclear Data foundation (JCKY2020201C157);the Anhui Project (Z010118169)

摘　　要：Nuclear charge density distribution plays an important role in both nuclear and atomic physics,for which the two-parameter Fermi(2pF)model has been widely applied as one of the most frequently used models.Currently,the feedforward neural network has been employed to study the available 2pF model parameters for 86 nuclei,and the accuracy and precision of the parameter-learning effect are improved by introducing A^(1∕3)into the input parameter of the neural network.Furthermore,the average result of multiple predictions is more reliable than the best result of a single prediction and there is no significant difference between the average result of the density and parameter values for the average charge density distribution.In addition,the 2pF parameters of 284(near)stable nuclei are predicted in this study,which provides a reference for the experiment.

关 键 词：Charge density distribution Two-parameter Fermi model Feedforward neural network approach 

分 类 号：O571[理学—粒子物理与原子核物理] TP183[理学—物理]