Prediction of nuclear charge density distribution with feedback neural network

• Published in: Nuclear science and techniques Vol. 33; no. 12; pp. 24 - 35
• Main Authors: Shang, Tian-Shuai, Li, Jian, Niu, Zhong-Ming
• Format: Journal Article
• Language: English
• Published: Singapore Springer Nature Singapore 01.12.2022; College of Physics,Jilin University,Changchun 130012,China%School of Physics and Optoelectronic Engineering,Anhui University,Hefei 230601,China
• Subjects: Beam Physics; Nuclear Energy; Particle Acceleration and Detection; Particle and Nuclear Physics; Physics; Physics and Astronomy; Charge density distribution; Two-parameter Fermi model; Feedforward neural network approach
• ISSN: 1001-8042; 2210-3147
• DOI: 10.1007/s41365-022-01140-9

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.