Model-Based Deep Learning Algorithm for Detection and Classification at High Event Rates

Pulse shape discrimination (PSD) is required for many radioactive particle monitoring applications. Classical PSD methods commonly struggle at high event rates in the presence of pile up, and are therefore utilized for low event rates. We present a PSD algorithm that combines classic approaches with deep learning techniques. The algorithm provides both detection and classification of the pulses at high event rates. While PSD algorithms for high event rates are often limited to two piled-up pulses, our algorithm is designed and tested for detection and classification under severe pile-up conditions, where three or more pulses are piled up. The algorithm was tested on both experimental data from a
$\text {Cs}_{{2}}\text {LiYCl6:Ce}$
(CLYC) detector and on synthetic data. The algorithm’s detection and discrimination performance is compared to current state-of-the-art methods. The algorithm’s performance is characterized under varying event rates, signal-to-noise ratio (SNR) conditions, and neutron-to-gamma event rate ratios.