Signal Discrimination in Thinned Silicon Neutron Detectors using Machine learning

Cheneler, David and Monk, Stephen and Anderson, Mike and Prendergast, David and Alhamdi, Mustafa (2019) Signal Discrimination in Thinned Silicon Neutron Detectors using Machine learning. In: 2019 IEEE Nuclear Science Symposium and Medical Imaging Conference, 2019-10-262019-11-02, Manchester Central Convention Centre.

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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.

Item Type: Contribution to Conference (Poster)
Journal or Publication Title: 2019 IEEE Nuclear Science Symposium and Medical Imaging Conference
Departments: Faculty of Science and Technology > Engineering
ID Code: 140317
Deposited By: ep_importer_pure
Deposited On: 14 Jan 2020 16:45
Refereed?: Yes
Published?: Published
Last Modified: 23 Feb 2020 06:56
URI: https://eprints.lancs.ac.uk/id/eprint/140317

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