论文标题
使用两个成分的高斯混合模型的脉搏堆积抑制方法，以脉冲形状区分闪烁体进行快速中子检测
Pulse Pileup Rejection Methods Using a Two-Component Gaussian Mixture Model for Fast Neutron Detection with Pulse Shape Discriminating Scintillator
论文作者
论文摘要
在许多情况下，脉搏形状区分闪烁体的材料使用户可以识别两种由两种颗粒引起的基本脉冲：中子和伽马群。一种用于构建分类器的简单解决方案是由从中子和伽马脉的集合中汲取的两组分组模型组成的。根据收集的要分类的数据条件，可能会发生多种类别的事件，除了中子和伽玛，最著名的是堆积事件。所有这类事件都是异常的，如果有疑问的粒子类别，则最好将它们从分析中删除。这项研究比较了几种机器学习和分析方法的性能，用于使用来自两个组分模型的分数来识别异常事件，尤其是去除堆积事件。这项研究的一个具体结果是提出一个新的异常评分，即表示G，该评分是从无监督的两组分模型中提出的，该模型在间隔[-1,1]上方便地分布。
Pulse shape discriminating scintillator materials in many cases allow the user to identify two basic kinds of pulses arising from two kinds of particles: neutrons and gammas. An uncomplicated solution for building a classifier consists of a two-component mixture model learned from a collection of pulses from neutrons and gammas at a range of energies. Depending on the conditions of data gathered to be classified, multiple classes of events besides neutrons and gammas may occur, most notably pileup events. All these kinds of events are anomalous and, in cases where the class of the particle is in doubt, it is preferable to remove them from the analysis. This study compares the performance of several machine learning and analytical methods for using the scores from the two-component model to identify anomalous events and in particular to remove pileup events. A specific outcome of this study is to propose a novel anomaly score, denoted G, from an unsupervised two-component model that is conveniently distributed on the interval [-1,1].
