Pulse shape discrimination, to distinguish between neutrons and gamma rays, is a very important classification task in thermonuclear fusion. Gaussian Mixture Models and probabilistic Support Vector Machines have been applied to hundreds of thousands of pulses obtained with a counter based on the NE213 liquid scintillator. The results of the two completely independent mathematical methods are in very good agreement, the maximum discrepancy being of the order of 2%. The achieved classification also shows an excellent value for the figure of merit, a Mahalanobis type of distance, implemented to quantify statistically the separation between the two particle distributions. These two machine learning tools provide also the probability of each example being a neutron or a gamma ray, allowing more detailed studies of the distribution of pulses. The proposed methodology therefore clearly outperforms previous techniques in practically all aspects of the classification.

区分中子和伽马射线的脉冲形状识别是热核聚变中非常重要的分类任务。高斯混合模型和概率支持向量机已应用于通过基于NE213液体闪烁器的计数器获得的数十万个脉冲。两种完全独立的数学方法的结果非常吻合，最大差异约为2％。所获得的分类还显示了品质因数（马氏距离类型）的极佳价值，该品质因数用于统计地量化两个粒子分布之间的距离。这两个机器学习工具还提供了每个例子是中子或伽马射线的可能性，从而允许对脉冲分布进行更详细的研究。