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Research output: Contribution to conference - Without ISBN/ISSN › Poster › peer-review
Signal Discrimination in Thinned Silicon Neutron Detectors using Machine learning. / Anderson, Mike; Prendergast, David; Alhamdi, Mustafa et al.
2019. Poster session presented at 2019 IEEE Nuclear Science Symposium and Medical Imaging Conference , Manchester, United Kingdom.Research output: Contribution to conference - Without ISBN/ISSN › Poster › peer-review
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TY - CONF
T1 - Signal Discrimination in Thinned Silicon Neutron Detectors using Machine learning
AU - Anderson, Mike
AU - Prendergast, David
AU - Alhamdi, Mustafa
AU - Cheneler, David
AU - Monk, Stephen
N1 - Conference code: 26
PY - 2019/10/26
Y1 - 2019/10/26
N2 - 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.
AB - 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.
M3 - Poster
T2 - 2019 IEEE Nuclear Science Symposium and Medical Imaging Conference
Y2 - 26 October 2019 through 2 November 2019
ER -