Artificial Neural Network Algorithm for Pulse Shape Discrimination in 2πα and 2πβ Particle Surface Emission Rate Measurements

分类： 物理学 >> 普通物理:统计和量子力学,量子信息等 提交时间： 2023-09-05

Li, Yuanqiao Zhu, Baoji Lv, Yang Zhu, Heng Lin, Min Chen, Kesheng Xu, Lijun

摘要： To enhance the accuracy of 2 and 2 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 and particles. Their discrimination efficacy is assessed by simulating standard pulse signals and those produced by contaminated sources, mixing and 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 and 2 particles. The BP neural network realizes approximately 99% accuracy for 2 particles and approximately 95% for 2 particles, thus surpassing the GA-BP's performance. Additionally, the results suggest enhancing particle discrimination accuracy by increasing the digital acquisition card's threshold lower limit. This study offers an advanced solution for the 2 and 2 surface emission rate measurement method, presenting superior adaptability and scalability over conventional techniques.

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