  In particle physics experiments, pulse shape discrimination (PSD) is a
powerful tool for eliminating the major background from signals. However, the
analysis methods have been a bottleneck to improving PSD performance. In this
study, two machine learning methods -- multilayer perceptron and convolutional
neural network -- were applied to PSD, and their PSD performance was compared
with that of conventional analysis methods. Three calcium-based halide
scintillators were grown using the vertical Bridgman--Stockbarger method and
used for the evaluation of PSD. Compared with conventional analysis methods,
the machine learning methods achieved better PSD performance for all the
scintillators. For scintillators with low light output, the machine learning
methods were more effective for PSD accuracy than the conventional methods in
the low-energy region.
