Paper
9 September 2019 Consistent principles for particle identification by pulse shape discriminating systems
Author Affiliations +
Abstract
Identifying particles that interact in materials that exhibit pulse-shape discrimination (PSD) is a statistical classifier problem. The field of statistical classifiers provides a toolkit of elements and principles that PSD can employ and re-use regardless of the detector material and read-out. Journals have published a myriad of PSD papers over the decades featuring useful components and concepts for implementing and/or improving particle identification. This paper categorizes and assembles PSD methods into one consistent taxonomy. Among the essential elements to consider when building a classifier, one encounters features, pre-processed and reduced features, labels, contamination, coverage, the model, the classifier, optimization and performance metrics, training, testing, scoring, performance trade curves, and thresholding.
© (2019) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Ronald E. Wurtz "Consistent principles for particle identification by pulse shape discriminating systems", Proc. SPIE 11114, Hard X-Ray, Gamma-Ray, and Neutron Detector Physics XXI, 111140X (9 September 2019); https://doi.org/10.1117/12.2528898
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Cited by 1 scholarly publication.
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KEYWORDS
Data modeling

Particles

Statistical analysis

Performance modeling

Statistical modeling

Contamination

Mathematical modeling

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