Pulse Shape Discrimination Algorithms: Survey and Benchmark

• URL: http://arxiv.org/abs/2508.02750v1
• Date: Sun, 03 Aug 2025 04:41:32 GMT
• Title: Pulse Shape Discrimination Algorithms: Survey and Benchmark
• Authors: Haoran Liu, Yihan Zhan, Mingzhe Liu, Yanhua Liu, Peng Li, Zhuo Zuo, Bingqi Liu, Runxi Liu,
• Abstract summary: This review presents a comprehensive survey and benchmark of pulse shape discrimination (PSD) algorithms for radiation detection.<n>We implement and evaluate all on two standardized datasets, using metrics including Figure of Merit (FOM), F1-score, ROC-AUC, and inter-method correlations.<n>Deep learning models, particularly Multi-Layer Perceptrons (MLPs) and hybrid approaches combining statistical features with neural regression, often outperform traditional methods.
• Score: 7.302101804475471
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: This review presents a comprehensive survey and benchmark of pulse shape discrimination (PSD) algorithms for radiation detection, classifying nearly sixty methods into statistical (time-domain, frequency-domain, neural network-based) and prior-knowledge (machine learning, deep learning) paradigms. We implement and evaluate all algorithms on two standardized datasets: an unlabeled set from a 241Am-9Be source and a time-of-flight labeled set from a 238Pu-9Be source, using metrics including Figure of Merit (FOM), F1-score, ROC-AUC, and inter-method correlations. Our analysis reveals that deep learning models, particularly Multi-Layer Perceptrons (MLPs) and hybrid approaches combining statistical features with neural regression, often outperform traditional methods. We discuss architectural suitabilities, the limitations of FOM, alternative evaluation metrics, and performance across energy thresholds. Accompanying this work, we release an open-source toolbox in Python and MATLAB, along with the datasets, to promote reproducibility and advance PSD research.

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