Pulse Shape Simulation and Discrimination using Machine-Learning Techniques

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.