脉冲形状鉴别Scintillator快中子探测中的双分量高斯混合模型脉冲堆积抑制方法
Pulse Pileup Rejection Methods Using a Two-Component Gaussian Mixture Model for Fast Neutron Detection with Pulse Shape Discriminating Scintillator
摘要：
在许多情况下，脉冲形状辨别闪烁体材料允许用户识别由两种粒子产生的两种基本脉冲：中子和伽马。用于构建分类器的简单解决方案包括从一系列能量的中子和伽马脉冲的集合中学习的双组分混合模型。根据收集到的待分类的数据的条件，可能发生除中子和伽马之外的多个类别的事件，最显著的是堆积事件。所有这些类型的事件都是反常的，在粒子的类别有疑问的情况下，最好将它们从分析中删除。本研究比较了几种机器学习和分析方法的性能，这些方法使用双组分模型的分数来识别异常事件，特别是去除堆积事件。本研究的一个具体成果是提出一种新的异常分数，表示为G，从一个无监督的两个组件模型，方便地分布在区间[-1，1]。
英文摘要：
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].
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
