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Signal Discrimination in Thinned Silicon Neutron Detectors using Machine learning

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

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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

Harvard

Anderson, M, Prendergast, D, Alhamdi, M, Cheneler, D & Monk, S 2019, 'Signal Discrimination in Thinned Silicon Neutron Detectors using Machine learning', 2019 IEEE Nuclear Science Symposium and Medical Imaging Conference , Manchester, United Kingdom, 26/10/19 - 2/11/19.

APA

Anderson, M., Prendergast, D., Alhamdi, M., Cheneler, D., & Monk, S. (2019). Signal Discrimination in Thinned Silicon Neutron Detectors using Machine learning. Poster session presented at 2019 IEEE Nuclear Science Symposium and Medical Imaging Conference , Manchester, United Kingdom.

Vancouver

Anderson M, Prendergast D, Alhamdi M, Cheneler D, Monk S. Signal Discrimination in Thinned Silicon Neutron Detectors using Machine learning. 2019. Poster session presented at 2019 IEEE Nuclear Science Symposium and Medical Imaging Conference , Manchester, United Kingdom.

Author

Anderson, Mike ; Prendergast, David ; Alhamdi, Mustafa et al. / Signal Discrimination in Thinned Silicon Neutron Detectors using Machine learning. Poster session presented at 2019 IEEE Nuclear Science Symposium and Medical Imaging Conference , Manchester, United Kingdom.

Bibtex

@conference{180d46a7a23b46aca3a09a3b94e4473e,
title = "Signal Discrimination in Thinned Silicon Neutron Detectors using Machine learning",
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.",
author = "Mike Anderson and David Prendergast and Mustafa Alhamdi and David Cheneler and Stephen Monk",
year = "2019",
month = oct,
day = "26",
language = "English",
note = "2019 IEEE Nuclear Science Symposium and Medical Imaging Conference , IEEE NSS/MIC 2019 ; Conference date: 26-10-2019 Through 02-11-2019",

}

RIS

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 -