Nuclear mass based on the multi-task learning neural network method

Xing-Chen Ming; Hong-Fei Zhang; Rui-Rui Xu; Xiao-Dong Sun; Yuan Tian; Zhi-Gang Ge

doi:10.1007/s41365-022-01031-z

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Nuclear mass based on the multi-task learning neural network method

NUCLEAR PHYSICS AND INTERDISCIPLINARY RESEARCH | Updated：2022-12-16

- Nuclear mass based on the multi-task learning neural network method
- Nuclear mass based on the multi-task learning neural network method
- 核技术（英文版） 2022年33卷第4期文章编号：48
- Affiliations：
  
  1.School of Nuclear Science and Technology, Lanzhou University, Lanzhou 730000, China
  2.China Nuclear Data Center, China Institute of Atomic Energy, Beijing 102413, China
- Author bio：
  
  mingxch19@lzu.edu.cn
  zhanghongfei@lzu.edu.cn
  xuruirui@ciae.ac.cn
- Funds：
  
  National Natural Science Foundation of China(1187050492;12005303;12175170)
- DOI：10.1007/s41365-022-01031-z
  中图分类号：
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Nuclear mass based on the multi-task learning neural network method[J]. 核技术（英文版）, 2022,33(4):48

Xing-Chen Ming, Hong-Fei Zhang, Rui-Rui Xu, et al. Nuclear mass based on the multi-task learning neural network method[J]. Nuclear Science and Techniques, 2022,33(4):48
Nuclear mass based on the multi-task learning neural network method[J]. 核技术（英文版）, 2022,33(4):48 DOI： 10.1007/s41365-022-01031-z.

Xing-Chen Ming, Hong-Fei Zhang, Rui-Rui Xu, et al. Nuclear mass based on the multi-task learning neural network method[J]. Nuclear Science and Techniques, 2022,33(4):48 DOI： 10.1007/s41365-022-01031-z.

摘要

Abstract

The global nuclear mass based on the macroscopic-microscopic model was studied by applying a newly designed multitask-learning artificial neural network (MTL-ANN). First, the reported nuclear binding energies of 2095 nuclei (,Z, ≥ 8,N, ≥ 8) released in the latest Atomic Mass Evaluation AME2020 and the deviations between the fitting result of the liquid drop model(LDM) and data from AME2020 for each nucleus were obtained. To compensate for the deviations and investigate the possible ignored physics in the LDM, the MTL-ANN method was introduced in the model. Compared to the single-task-learning (STL) method, this new network has a powerful ability to simultaneously learn multi-nuclear properties, such as the binding energies and single neutron and proton separation energies. Moreover, it is highly effective in reducing the risk of overfitting and achieving better predictions. Consequently, good predictions can be obtained using this nuclear mass model for both the training and validation datasets and for the testing dataset. In detail, the global root mean square (RMS) of the binding energy is effectively reduced from approximately 2.4 MeV of LDM to the current 0.2 MeV, and the RMS of ,S,n,S,p, can also reach approximately 0.2 MeV. Moreover, compared to STL, for the training and validation sets, 3∼9% improvement can be achieved with the binding energy, and 20∼30% improvement for ,S,n,S,p,; for the testing sets, the reduction in deviations can even reach 30∼40%, which significantly illustrates the advantage of the current MTL.

关键词

Keywords

Macroscopic-microscopic modelBinding energyNeural networkMulti-task learning

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