Chinese Physics C > 2025, Vol. 49 > Issue(9): 094111 DOI: 10.1088/1674-1137/add10a

Predictions of unknown masses using a feedforward neural network

1.
School of Science, Shanghai Maritime University, Shanghai 201306, China
2.
School of Nuclear Science and Technology, Southwest University of Science and Technology, Mianyang 621010, China

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Abstract：
In this study, a feedforward neural network (FNN) approach is employed to optimize three local mass models (GK, GKs, and GK+J). We find that adding physical quantities related to the pairing effect in the input layer can effectively improve the prediction accuracy of local models. For the known masses in AME2012, the FNN reduces the root-mean-square deviation between theory and experiment for the three mass models by 11 keV, 32 keV, and 623 keV. Among them, the improvement effect of the light mass region with mass number between 16 and 60 is better than that of medium and heavy mass regions. The approach also has good optimization results when extrapolating AME2012 to AME2020 and the latest measured masses after AME2020. Based on the improved mass data, the separation energies for single- and two-proton (neutron) emissions and α-decay energies are obtained, which agree well with the experiment.
- atomic mass,
- local mass model,
- feedforward neural network

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Cheng-wei Dai, Hui Jiang and Yang Lei. Predictions of unknown masses in the feedforward neural network[J]. Chinese Physics C. doi: 10.1088/1674-1137/add10a

Cheng-wei Dai, Hui Jiang and Yang Lei. Predictions of unknown masses in the feedforward neural network[J]. Chinese Physics C. doi:10.1088/1674-1137/add10a shu

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Predictions of unknown masses using a feedforward neural network

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