本研究基于三个具有代表性的理论模型:相对论连续谱Hartree-Bogoliubov (RCHB)理论,相对论平均场(RMF)理论,Skyrme-Hartree-Fock-Bogoliubov (SHFB)模型,首先介绍了人工神经网络(ANN)方法,计算出了三个模型的单核子分离能的理论预测值...本研究基于三个具有代表性的理论模型:相对论连续谱Hartree-Bogoliubov (RCHB)理论,相对论平均场(RMF)理论,Skyrme-Hartree-Fock-Bogoliubov (SHFB)模型,首先介绍了人工神经网络(ANN)方法,计算出了三个模型的单核子分离能的理论预测值。随后利用神经网络对单核子分离能的理论值进行了优化训练,降低了单核子分离能的理论预测值与实验值之间的均方根偏差(RMSD),并在此基础上进行了两种分区优化,分别为质子和中子的幻数分区,分区优化训练后进一步降低了RMSD。单核子分离能分区训练后的RMSD比整体直接训练的效果更好,特别能显著降低轻核区的RMSD,单中子分离能进行中子幻数分区训练的效果更好,单质子分离能进行质子幻数分区训练的效果更好。This research is based on three representative theoretical models: the Relativistic Continuum Hartree-Bogoliubov (RCHB) theory, Relativistic Mean Field (RMF) theory, and Skyrme-Hartree-Fock-Bogoliubov (SHFB) model. First, the Artificial Neural Network (ANN) method was introduced to calculate theoretical predictions of single-nucleon separation energies for these three models. Subsequently, the neural network was employed to optimize and train the theoretical values of single-nucleon separation energies, reducing the root mean square deviation (RMSD) between theoretical predictions and experimental values. Two partitioning optimization schemes were then implemented: proton magic number partitioning and neutron magic number partitioning. The partitioned optimization training further reduced RMSD values. The partitioned training of single-nucleon separation energies demonstrated better performance than direct global training, particularly in significantly reducing RMSD in the light nuclei region. Specifically, neutron magic number partitioning showed superior effectiveness for optimizing single-neutron separation energies, while proton magic number partitioning yielded better results for single-proton separation energies.展开更多
核子配对近似模型是研究原子核性质的重要方法之一,该方法已经在许多核领域内取得了重要成果。在对核129Sn的研究过程中,添加非集体对参数,并将该参数进行调整,从而观察非集体配对方式对核子能态的影响。经过计算后,发现非集体对主要对...核子配对近似模型是研究原子核性质的重要方法之一,该方法已经在许多核领域内取得了重要成果。在对核129Sn的研究过程中,添加非集体对参数,并将该参数进行调整,从而观察非集体配对方式对核子能态的影响。经过计算后,发现非集体对主要对负宇称能态有较大影响,而在正宇称能态中对角动量较高的能态有较大的影响。The nucleon pair approximation shell model is one of the most important methods for studying the properties of atomic nuclei, and the method has yielded important results in many nuclear fields. During the study of nuclear 129Sn, the non-collective pairing parameter is added and the parameter is tuned so as to observe the effect of the non-collective pairing approach on the energy states of nuclei. After calculations, it is found that the non-collective pairing has a large effect mainly on the negative-universal energy states, while in the positive-universal energy states it has a large effect on the energy states with higher angular momentum.展开更多
文摘本研究基于三个具有代表性的理论模型:相对论连续谱Hartree-Bogoliubov (RCHB)理论,相对论平均场(RMF)理论,Skyrme-Hartree-Fock-Bogoliubov (SHFB)模型,首先介绍了人工神经网络(ANN)方法,计算出了三个模型的单核子分离能的理论预测值。随后利用神经网络对单核子分离能的理论值进行了优化训练,降低了单核子分离能的理论预测值与实验值之间的均方根偏差(RMSD),并在此基础上进行了两种分区优化,分别为质子和中子的幻数分区,分区优化训练后进一步降低了RMSD。单核子分离能分区训练后的RMSD比整体直接训练的效果更好,特别能显著降低轻核区的RMSD,单中子分离能进行中子幻数分区训练的效果更好,单质子分离能进行质子幻数分区训练的效果更好。This research is based on three representative theoretical models: the Relativistic Continuum Hartree-Bogoliubov (RCHB) theory, Relativistic Mean Field (RMF) theory, and Skyrme-Hartree-Fock-Bogoliubov (SHFB) model. First, the Artificial Neural Network (ANN) method was introduced to calculate theoretical predictions of single-nucleon separation energies for these three models. Subsequently, the neural network was employed to optimize and train the theoretical values of single-nucleon separation energies, reducing the root mean square deviation (RMSD) between theoretical predictions and experimental values. Two partitioning optimization schemes were then implemented: proton magic number partitioning and neutron magic number partitioning. The partitioned optimization training further reduced RMSD values. The partitioned training of single-nucleon separation energies demonstrated better performance than direct global training, particularly in significantly reducing RMSD in the light nuclei region. Specifically, neutron magic number partitioning showed superior effectiveness for optimizing single-neutron separation energies, while proton magic number partitioning yielded better results for single-proton separation energies.
文摘核子配对近似模型是研究原子核性质的重要方法之一,该方法已经在许多核领域内取得了重要成果。在对核129Sn的研究过程中,添加非集体对参数,并将该参数进行调整,从而观察非集体配对方式对核子能态的影响。经过计算后,发现非集体对主要对负宇称能态有较大影响,而在正宇称能态中对角动量较高的能态有较大的影响。The nucleon pair approximation shell model is one of the most important methods for studying the properties of atomic nuclei, and the method has yielded important results in many nuclear fields. During the study of nuclear 129Sn, the non-collective pairing parameter is added and the parameter is tuned so as to observe the effect of the non-collective pairing approach on the energy states of nuclei. After calculations, it is found that the non-collective pairing has a large effect mainly on the negative-universal energy states, while in the positive-universal energy states it has a large effect on the energy states with higher angular momentum.
