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This study shows an implementation of neutron-gamma pulse shapediscrimination (PSD) using a two-dimensional convolutional neural network. Theinputs to the network are snapshots of the unprocessed, digitized signals froma BC501A detector. By exposing a BC501A detector to a Cf-252 source, neutronand gamma signals were collected to create a training dataset. The realisticdatasets were created using a data-driven approach for labeling the digitizedsignals, having classified snapshots of neutron and gamma pulses. Our algorithmwas able to successfully differentiate neutrons and gammas with similaraccuracy as the CI approach. Additionally, the independent dataset accuracy forour suggested 2D CNN-based PSD approach is 99%. In contrast to the traditionalcharge integration method, our suggested algorithm with data augmentation, iscapable of extracting features from snapshots of the raw data based on thesignal structures, making it computationally more efficient and alsoappropriate for other types of neutron detectors.

Neutron-Gamma Pulse Shape Discrimination for Organic Scintillation Detector using 2D CNN based Image Classification