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Prediction of nuclear charge density distribution with feedback neural network

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Abstract

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.

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  1. College of Physics, Jilin University, Changchun, 130012, China

    Tian-Shuai Shang & Jian Li

  2. School of Physics and Optoelectronic Engineering, Anhui University, Hefei, 230601, China

    Zhong-Ming Niu

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All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by Tian-Shuai Shang, Jian Li and Zhong-Ming Niu. The first draft of the manuscript was written by Tian-Shuai Shang and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

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

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Shang, TS., Li, J. & Niu, ZM. Prediction of nuclear charge density distribution with feedback neural network. NUCL SCI TECH 33, 153 (2022). https://doi.org/10.1007/s41365-022-01140-9

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  • DOI: https://doi.org/10.1007/s41365-022-01140-9

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