Artificial neural network algorithm for pulse shape discrimination in 2Ц and 2Ц particle surface emission rate measurements

To enhance the accuracy of 2 pi alpha and 2 pi beta particle surface emission rate measurements and address the identification issues of nuclides in conventional methods, this study introduces two artificial neural network (ANN) algorithms: back-propagation (BP) and genetic algorithm-based back-propagation (GA-BP). These algorithms classify pulse signals from distinct alpha and beta particles. Their discrimination efficacy is assessed by simulating standard pulse signals and those produced by contaminated sources, mixing alpha and beta particles within the detector. This study initially showcases energy spectrum measurement outcomes, subsequently tests the ANNs on the measurement and validation datasets, and contrasts the pulse shape discrimination efficacy of both algorithms. Experimental findings reveal that the proportional counter's energy resolution is not ideal, thus rendering energy analysis insufficient for distinguishing between 2 pi alpha and 2 pi beta particles. The BP neural network realizes approximately 99% accuracy for 2 pi alpha particles and approximately 95% for 2 pi beta particles, thus surpassing the GA-BP's performance. Additionally, the results suggest enhancing beta particle discrimination accuracy by increasing the digital acquisition card's threshold lower limit. This study offers an advanced solution for the 2 pi alpha and 2 pi beta surface emission rate measurement method, presenting superior adaptability and scalability over conventional techniques.