Abstract:
The discrimination between neutron and gamma radiation pulses is crucial in mixed environment for neutron spectroscopy, particularly in fields such as nuclear science, nuclear safety, environmental monitoring, and radiation imaging. A quantitative measurement is essential to evaluate the discriminatory performance and a generalized yardstick is desirable for all the available methods. This study introduces a semi-supervised machine learning approach utilizing Multi-Layer Perceptron, Convolutional Neural Network, Long Short-Term Memory Network and Transformer encoder-based classifier to perform neutron-gamma pulse discrimination. The proposed model is applied to pulse signals acquired from a liquid scintillator BC501A coupled with a photomultiplier tube R4144, recognized for their high sensitivity and effectiveness in neutron-gamma discrimination tasks. The model's performance is rigorously evaluated against traditional analogue and digital charge comparison discrimination techniques. A generalized method is introduced in terms of figure of merit for equipollent discrimination performance comparison with existing analog and digital-based methods as well as various other machine learning based classification techniques.