Optimization of Nuclear Mass Models Using Algorithms and Neural Networks

2024/09/18 by Jin Li, Hang Yang, Li, Jin +1
Engineering · #Advanced Data Processing Techniques #FOS: Physical sciences #Nuclear Theory (nucl-th)

paper · pdf · doi:10.48550/arxiv.2409.11930

openalex publication_date 2024/09/18 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

Taking into account nucleon-nucleon gravitational interaction, higher-order terms of symmetry energy, pairing interaction, and neural network corrections, a new BW4 mass model has been developed, which more accurately reflects the contributions of various terms to the binding energy. A novel hybrid algorithm and neural network correction method has been implemented to optimize the discrepancy between theoretical and experimental results, significantly improving the model's binding energy predictions (reduced to around 350 keV). At the same time, the theoretical accuracy near magic nuclei has been marginally enhanced, effectively capturing the special interaction effects around magic nuclei and showing good agreement with experimental data.

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