Predictions of nuclear charge radii based on the convolutional neural network

• 作者： Cao, Yingyu ¹ Guo, Jianyou ² Zhou, Bo ^1,3
• 作者单位：
  1.  Key Laboratory of Nuclear Physics and Ion-beam Application (MOE), Institute of Modern Physics, Fudan University, Shanghai 200433, China
  2.  School of Physics and Optoelectronics Engineering, Anhui University, Hefei 230601, China
  3.  Shanghai Research Center for Theoretical Nuclear Physics, NSFC and Fudan University, Shanghai 200438, China
• 通讯作者： Zhou, Bo  Email:zhou_bo@fudan.edu.cn
• 提交时间：2023-09-07

摘要: In this study, we developed a neural network that incorporates a fully connected layer with a convolutional layer to predict the nuclear charge radii based on the relationships between four local nuclear charge radii.The convolutional neural network (CNN) combines the isospin and pairing effects to describe the charge radii of nuclei with $A geq $ 39 and $Z geq $ 20. The developed neural network achieved a root--mean--square (RMS) deviation of 0.0195 fm for a dataset with 928 nuclei. Specifically, the CNN reproduced the trend of the inverted parabolic behavior and odd--even staggering observed in the calcium isotopic chain, demonstrating reliable predictive capability.

• 来自： caoyingyu
• 分类： 物理学 >> 核物理学
• 投稿状态： 已被期刊接收
• 引用： ChinaXiv:202309.00079 (或此版本 ChinaXiv:202309.00079V1)
  DOI:10.12074/202309.00079V1
  CSTR:32003.36.ChinaXiv.202309.00079.V1
• 推荐引用方式： Cao, Yingyu,Guo, Jianyou,Zhou, Bo.(2023).Predictions of nuclear charge radii based on the convolutional neural network.中国科学院科技论文预发布平台.doi:10.12074/202309.00079V1 (点此复制)