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Consistent principles for particle ID from PSD systems
Author(s): Ronald E. Wurtz
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Paper 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.

Paper Details

Date Published: 9 September 2019
PDF: 14 pages
Proc. SPIE 11114, Hard X-Ray, Gamma-Ray, and Neutron Detector Physics XXI, 111140X (9 September 2019); doi: 10.1117/12.2528898
Show Author Affiliations
Ronald E. Wurtz, Lawrence Livermore National Lab. (United States)


Published in SPIE Proceedings Vol. 11114:
Hard X-Ray, Gamma-Ray, and Neutron Detector Physics XXI
Ralph B. James; Arnold Burger; Stephen A. Payne, Editor(s)

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