论文标题
核质量预测随着机器学习达到$ r $ process研究所需的准确性
Nuclear mass predictions with machine learning reaching the accuracy required by $r$-process studies
论文作者
论文摘要
通过学习均匀的核的质量表面以及与其相邻核的相关能量，可以用贝叶斯神经网络预测核肿块。通过将已知的物理学保持在各种复杂的质量模型中并执行神经网络的精致设计，拟议的贝叶斯机器学习（BML）质量模型的准确性达到了$ 84 $ 〜KEV，这跨越了实验已知区域中$ 100 $ 〜KEV的准确性阈值。还证明，对质量预测的相应不确定性进行了适当的评估，而不确定性沿着同位素链向未知区域的同位链增加约50美元$ 〜KEV。已知区域中的外壳结构得到了很好的描述，预计未知区域中的几个重要特征，例如$ n = 40 $的新魔术数，$ n = 82 $ shell的稳健性，$ n = 126 $ shell的淬火以及$ n = 104 $的平滑分离能量。
Nuclear masses are predicted with the Bayesian neural networks by learning the mass surface of even-even nuclei and the correlation energies to their neighbouring nuclei. By keeping the known physics in various sophisticated mass models and performing the delicate design of neural networks, the proposed Bayesian machine learning (BML) mass model achieves an accuracy of $84$~keV, which crosses the accuracy threshold of the $100$~keV in the experimentally known region. It is also demonstrated the corresponding uncertainties of mass predictions are properly evaluated, while the uncertainties increase by about $50$~keV each step along the isotopic chains towards the unknown region. The shell structures in the known region are well described and several important features in the unknown region are predicted, such as the new magic numbers around $N = 40$, the robustness of $N = 82$ shell, the quenching of $N = 126$ shell, and the smooth separation energies around $N = 104$.
