论文标题
用混合物密度网络量化裂变碎片质量产量的不确定性
Quantifying Uncertainties on Fission Fragment Mass Yields With Mixture Density Networks
论文作者
论文摘要
概率机器学习技术可以学习输入特征和关注的输出量之间的复杂关系，并考虑到数据集中的随机性或不确定性。在这项最初的工作中，我们探讨了一个这样的概率网络，即混合密度网络（MDN）来再现裂变产率及其不确定性。我们研究了$^{252} $ CF自发裂变的质量收益率，探讨了收敛预测所需的训练样本数量，不同水平的不确定性从培训集合到MDN预测的繁殖程度如何，以及产量的物理约束（例如正常化和对称性）的良好约束，例如，量身定期和对称性 - 由Algorithm固定。最后，我们测试了MDN在$^{235} $ U上使用能量依赖的质量收益率的训练集中的MDN插值和外推超出样品的能力。 MDN提供了一种可靠的方法来包括和预测不确定性，并且是补充稀疏核数据集的前进道路。
Probabilistic machine learning techniques can learn both complex relations between input features and output quantities of interest as well as take into account stochasticity or uncertainty within a data set. In this initial work, we explore the use of one such probabilistic network, the Mixture Density Network (MDN), to reproduce fission yields and their uncertainties. We study mass yields for the spontaneous fission of $^{252}$Cf, exploring the number of training samples needed for converged predictions, how different levels of uncertainty propagate from the training set to the MDN predictions, and how well physical constraints of the yields - such as normalization and symmetry - are upheld by the algorithm. Finally, we test the ability of the MDN to interpolate between and extrapolate beyond samples in the training set using energy-dependent mass yields for the neutron-induced fission on $^{235}$U. The MDN provides a reliable way to include and predict uncertainties and is a promising path forward for supplementing sparse sets of nuclear data.
