Title: Optimization of Nuclear Mass Models Using Algorithms and Neural Networks Show Chinese title

Title: 基于算法和神经网络的核质量模型优化

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. Show Chinese abstract

Abstract: 考虑到核子-核子引力相互作用、对称能的高阶项、配对相互作用和神经网络修正，开发了一种新的BW4质量模型，该模型更准确地反映了各项贡献对结合能的影响。 一种新颖的混合算法和神经网络修正方法已被实施，以优化理论结果与实验结果之间的差异，显著提高了模型的结合能预测（降低到约350 keV）。 同时，靠近幻数核的理论准确性得到了小幅提升，有效捕捉了幻数核周围的特殊相互作用效应，并与实验数据表现出良好的一致性。