  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.
