  This study shows an implementation of neutron-gamma pulse shape
discrimination (PSD) using a two-dimensional convolutional neural network. The
inputs to the network are snapshots of the unprocessed, digitized signals from
a BC501A detector. By exposing a BC501A detector to a Cf-252 source, neutron
and gamma signals were collected to create a training dataset. The realistic
datasets were created using a data-driven approach for labeling the digitized
signals, having classified snapshots of neutron and gamma pulses. Our algorithm
was able to successfully differentiate neutrons and gammas with similar
accuracy as the CI approach. Additionally, the independent dataset accuracy for
our suggested 2D CNN-based PSD approach is 99%. In contrast to the traditional
charge integration method, our suggested algorithm with data augmentation, is
capable of extracting features from snapshots of the raw data based on the
signal structures, making it computationally more efficient and also
appropriate for other types of neutron detectors.

Instrumentation and Detectors;Nuclear Experiment