An essential metric for the quality of a particle-identification experiment is its statistical power to discriminate between signal and background. Pulse shape discrimination (PSD) is a basic method for this purpose in many nuclear, high-energy, and rare-event search experiments where scintillator detectors are used. Conventional techniques exploit the difference between decay-times of the pulse from signal and background events or pulse signals caused by different types of radiation quanta to achieve good discrimination. However, such techniques are efficient only when the total light-emission is sufficient to get a proper pulse profile. This is only possible when there is significant recoil energy due to the incident particle in the detector. But, rare-event search experiments like neutrino or dark-matter direct search experiments don't always satisfy these conditions. Hence, it becomes imperative to have a method that can deliver very efficient discrimination in these scenarios. Neural network-based machine-learning algorithms have been used for classification problems in many areas of physics, especially in high-energy experiments, and have given better results compared to conventional techniques. We present the results of our investigations of two network-based methods viz. Dense Neural Network and Recurrent Neural Network, for pulse shape discrimination and compare the same with conventional methods.
翻译:粒子识别实验质量的基本衡量标准是其统计能力,以区分信号和背景。在许多使用焚化器探测器的核、高能和稀有活动搜索实验中,脉冲形状歧视(PSD)是用于此目的的基本方法。常规技术利用信号和背景事件产生的脉冲衰变时间或不同种类辐射夸特造成的脉冲信号之间的差别,以实现良好的区别。然而,只有当光导总排放量足以取得适当的脉冲剖面时,这种技术才有效。只有在探测器中事件粒子产生大量后座能量时,才可能。但是,微微子或暗物质直接搜索实验等稀有活动的搜索实验总是不能满足这些条件。因此,有必要采用一种能够在这些情景中带来非常有效歧视的方法。基于神经网络的机器学习算法已经用于许多物理领域的分类问题,特别是在高能量实验中,并且与常规技术相比,已经取得了更好的结果。我们用两个基于网络的方法调查的结果来对比常规系统(例如神经网络)的磁性分析。