论文标题
从概率神经网络中学到的核群众
Nuclear masses learned from a probabilistic neural network
论文作者
论文摘要
在过去的几年中，机器学习方法和不确定性量化一直在低能核物理学中引起人们的兴趣。特别是，高斯过程和贝叶斯神经网络越来越多地用于改善质量模型预测，同时提供良好的不确定性。在这项工作中，我们使用概率混合物密度网络（MDN）直接预测测量数据范围内2016年原子质量评估的质量过量，并且我们推断了推断的模型超出可用的实验数据。 MDN不仅提供平均值，而且提供训练集和推断测试集中的完整后验分布。我们表明，将物理信息添加到特征空间中增加了匹配的准确性到训练数据，并提供了超出实验数据限制的物理上有意义的外推。
Machine learning methods and uncertainty quantification have been gaining interest throughout the last several years in low-energy nuclear physics. In particular, Gaussian processes and Bayesian Neural Networks have increasingly been applied to improve mass model predictions while providing well-quantified uncertainties. In this work, we use the probabilistic Mixture Density Network (MDN) to directly predict the mass excess of the 2016 Atomic Mass Evaluation within the range of measured data, and we extrapolate the inferred models beyond available experimental data. The MDN not only provides mean values but also full posterior distributions both within the training set and extrapolated testing set. We show that the addition of physical information to the feature space increases the accuracy of the match to the training data as well as provides for more physically meaningful extrapolations beyond the the limits of experimental data.
