Pulse shape discrimination based on the Tempotron: a powerful classifier on GPU

• DOI: 10.48550/arxiv.2305.18205
• Publication Date: 2024-10-01
• Authors: Haoran Liu; Peng Li; Mingzhe Liu; Kaimin Wang; Zhuo Zuo; Bingqi Liu
• Keywords: Signal Processing (eess.SP); FOS: Computer and information sciences; Computer Science - Machine Learning; FOS: Electrical engineering, electronic engineering, information engineering; FOS: Physical sciences; Electrical Engineering and Systems Science - Signal Processing; Nuclear Experiment (nucl-ex); Nuclear Experiment; Machine Learning (cs.LG)

This study utilized the Tempotron, a robust classifier based on a third-generation neural network model, for pulse shape discrimination. By eliminating the need for manual feature extraction, the Tempotron model can process pulse signals directly, generating discrimination results based on prior knowledge. The study performed experiments using GPU acceleration, resulting in over 500 times faster compared to the CPU-based model, and investigated the impact of noise augmentation on the Tempotron performance. Experimental results substantiated that Tempotron serves as a formidable classifier, adept at accomplishing high discrimination accuracy on both AmBe and time-of-flight PuBe datasets. Furthermore, analyzing the neural activity of Tempotron during training shed light on its learning characteristics and aided in selecting its hyperparameters. Moreover, the study addressed the constraints and potential avenues for future development in utilizing the Tempotron for pulse shape discrimination. The dataset used in this study and the GPU-based Tempotron are publicly available on GitHub at https://github.com/HaoranLiu507/TempotronGPU.<br/>12 pages, 9 figures<br/>