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  • MA IEEE Poster 221019 V2

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    Available under license: CC BY-NC: Creative Commons Attribution-NonCommercial 4.0 International License

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Signal Discrimination in Thinned Silicon Neutron Detectors using Machine learning

Research output: Contribution to conference - Without ISBN/ISSN Poster

Published
Publication date26/10/2019
Original languageEnglish
Event2019 IEEE Nuclear Science Symposium and Medical Imaging Conference - Manchester Central Convention Centre, Manchester, United Kingdom
Duration: 26/10/20192/11/2019
Conference number: 26

Conference

Conference2019 IEEE Nuclear Science Symposium and Medical Imaging Conference
Abbreviated titleIEEE NSS/MIC 2019
CountryUnited Kingdom
CityManchester
Period26/10/192/11/19

Abstract

High gamma backgrounds can pose a significant source of interference in solid-state neutron detectors making the neutron flux approximation inaccurate.
This work focuses on optimizing a thin sensor thickness to enhance the neutron capture rate and reject gammas, and analysis of multiple input source through the differentiation of signals using pattern recognition.
Gamma isotopes and neutron spectrums have been simulated using GEANT4 + Electronic noise estimation. Different machine learning tools have been considered to discriminate different gamma and neutron sources, including PCA, RNN, SVM, KNN, ResNet and others.