Author:
Dutta S.,Ghosh S.,Bhattacharya S.,Saha S.
Abstract
Abstract
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
scintillation detectors are used. Conventional techniques exploit
the difference between decay-times of the pulses 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
adequate amount of energy is deposited from recoil of the electrons
or the nuclei of the scintillator materials caused by the incident
particle on the detector. But, rare-event search experiments like
direct search for dark matter do not always satisfy these
conditions. Hence, it becomes imperative to have a method that can
deliver a 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.
Subject
Mathematical Physics,Instrumentation