AI-Driven α/β Particle Discrimination for the Dual-Scintillator Detector

Abstract: Accurate measurement of the activities of α/β radionuclides in environmental samples is critical for environmental radiation monitoring. Traditional α/β discrimination methods for dualscintillator detectors primarily rely on the amplitude, width, and rise time of the pulse from the detector, often leading to crosstalk between α/β signals and compromising measurement accuracy. To address this limitation, this study designs a composite detector combining dualscintillators with silicon photomultiplier (SiPM) array and proposes a convolutional neural network (CNN) model for α/β particle signal discrimination. By constructing a lightweight CNN architecture, this method extracts multi-dimensional features from pulse shapes to reach the highprecision classification of α/β particles. Experimental validation using mixed signals from a 244 Cm α-source and a 90 Sr-90 Y β-source shows that the CNN method significantly reduces crosstalk ratios: the alpha-to-beta (α→β) crosstalk ratio is reduced to 0.25 %, and the beta-toalpha(β→α) crosstalk ratio to 0 %. Compared with traditional particle discrimination methods such as the Amplitude-and-width Discrimination (AWD) and Integral Rise Time Method (IRTM), these crosstalk ratios are reduced by one order of magnitude, demonstrating the method's superiority in improving measurement accuracy. This study provides an intelligent, low-cost, and scalable solution for high-precision detection of radioactive contamination in environmental samples, with broad prospects in environmental radiation monitoring.