论文标题
通过后传播神经网络对核电荷密度分布的校准
Calibration of nuclear charge density distribution by back-propagation neural networks
论文作者
论文摘要
基于后传播神经网络和密度功能理论，首先进行监督学习以产生核电荷密度分布。通过复合损耗函数将电荷密度进一步校准为实验电荷半径。据发现，当考虑均衡，配对和壳效应时，验证集中的核的$ 96 \％$落在预测电荷半径的两个标准偏差之内。然后将校准的电荷密度映射到物质密度，并根据Hohenberg-Kohn定理进一步映射到结合能。它提供了对结合能和电荷半径中某些核的改进描述。此外，在$^{48} $ ca中的异常过度结合意味着存在不可或缺的超越均值效果。
Based on the back-propagation neural networks and density functional theory, a supervised learning is performed firstly to generate the nuclear charge density distributions. The charge density is further calibrated to the experimental charge radii by a composite loss function. It is found that, when the parity, pairing, and shell effects are taken into account, about $96\%$ of the nuclei in the validation set fall within two standard deviations of the predicted charge radii. The calibrated charge density is then mapped to the matter density, and further mapped to the binding energies according to the Hohenberg-Kohn theorem. It provides an improved description of some nuclei in both binding energies and charge radii. Moreover, the anomalous overbinding in $^{48}$Ca implies the existence of an indispensable beyond-mean-field effect.
