Application of a Novel Machine Learning Approach to SiPM-Based Neutron/Gamma Detection and Discrimination
| Autor: | Marek Flaska, Azaree T. Lintereur, Marc A. Wonders, Matthew Durbin |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
010308 nuclear & particles physics
Computer science business.industry 020209 energy Detector 02 engineering and technology Scintillator Machine learning computer.software_genre 01 natural sciences Particle detector Shape parameter Data set Silicon photomultiplier 0103 physical sciences 0202 electrical engineering electronic engineering information engineering Figure of merit Neutron Artificial intelligence business computer |
| Zdroj: | 2019 IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC). |
| DOI: | 10.1109/nss/mic42101.2019.9059952 |
| Popis: | Silicon photomultipliers (SiPMs) have become a common component of radiation detection systems and have shown promise in distinguishing neutrons and gammas when coupled with detectors sensitive to both particle types. This work investigates the use of a novel machine learning (ML) approach to aid in this discrimination. The proposed ML algorithm performs a regression on a conventionally calculated pulse shape parameter (PSP), producing a more representative PSP while allowing for direct comparison to the traditional pulse shape discrimination (PSD) technique. This work also investigates how the proposed ML-based PSD approach can benefit different scintillator and light-sensor combinations with varying levels of traditional PSD performance, especially those based on SiPMs. A preliminary implementation of the regression method with a Hamamatsu SiPM and stilbene scintillator combination on a data set of mixed gamma and neutron pulses from 252Cf resulted in an increase in the figure of merit of approximately 100% compared to the traditional PSD technique. |
| Databáze: | OpenAIRE |
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