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Title: Artificial neural network algorithms for pulse shape discrimination and recovery of piled-up pulses in organic scintillators

Abstract

We developed two neural-network (NN)-based algorithms (fully-connected neural network (Fc-NN) and recurrent neural network (RNN)) to perform pulse shape discrimination (PSD) and identification of piled-up pulses produced by organic scintillators, upon interaction with neutrons and gamma rays. We tested the algorithms on measured and verification sets of data and compared their classification performances to standard approaches. At a high acquisition count rate (100,000 counts per second, cps), in the presence of a gamma-to-neutron ratio of approximately 400–1, the proposed NN-based algorithm achieves a fraction of misclassified neutron, gamma, and piled-up pulses of approximately 1%, 1.8%, and 0.6%, respectively. Compared to the traditional approach, it exhibits 3×, 14×, and 11× improved (lower) miscalculation rates for neutron, gamma, and piled-up pulses, respectively. Here, we also demonstrate the capability of NN-based algorithms of successfully recovering and identifying neutron and gamma ray compositions from piled-up pulses in challenging, high pulse count rate conditions.

Authors:
 [1];  [1];  [1];  [1];  [1];  [1]
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  1. Univ. of Michigan, Ann Arbor, MI (United States)
Publication Date:
Research Org.:
Univ. of Michigan, Ann Arbor, MI (United States)
Sponsoring Org.:
USDOE National Nuclear Security Administration (NNSA)
OSTI Identifier:
1798642
Alternate Identifier(s):
OSTI ID: 1582705
Grant/Contract Number:  
NA0002534
Resource Type:
Accepted Manuscript
Journal Name:
Annals of Nuclear Energy (Oxford)
Additional Journal Information:
Journal Name: Annals of Nuclear Energy (Oxford); Journal Volume: 120; Journal ID: ISSN 0306-4549
Publisher:
Elsevier
Country of Publication:
United States
Language:
English
Subject:
42 ENGINEERING; Neural networks; Organic scintillators; Piled-up identification; Pulse shape discrimination

Citation Formats

Fu, C., Di Fulvio, A., Clarke, S. D., Wentzloff, D., Pozzi, S. A., and Kim, H. S. Artificial neural network algorithms for pulse shape discrimination and recovery of piled-up pulses in organic scintillators. United States: N. p., 2018. Web. doi:10.1016/j.anucene.2018.05.054.
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Fu, C., Di Fulvio, A., Clarke, S. D., Wentzloff, D., Pozzi, S. A., & Kim, H. S. Artificial neural network algorithms for pulse shape discrimination and recovery of piled-up pulses in organic scintillators. United States. https://doi.org/10.1016/j.anucene.2018.05.054
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Fu, C., Di Fulvio, A., Clarke, S. D., Wentzloff, D., Pozzi, S. A., and Kim, H. S. Thu . "Artificial neural network algorithms for pulse shape discrimination and recovery of piled-up pulses in organic scintillators". United States. https://doi.org/10.1016/j.anucene.2018.05.054. https://www.osti.gov/servlets/purl/1798642.
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@article{osti_1798642,
title = {Artificial neural network algorithms for pulse shape discrimination and recovery of piled-up pulses in organic scintillators},
author = {Fu, C. and Di Fulvio, A. and Clarke, S. D. and Wentzloff, D. and Pozzi, S. A. and Kim, H. S.},
abstractNote = {We developed two neural-network (NN)-based algorithms (fully-connected neural network (Fc-NN) and recurrent neural network (RNN)) to perform pulse shape discrimination (PSD) and identification of piled-up pulses produced by organic scintillators, upon interaction with neutrons and gamma rays. We tested the algorithms on measured and verification sets of data and compared their classification performances to standard approaches. At a high acquisition count rate (100,000 counts per second, cps), in the presence of a gamma-to-neutron ratio of approximately 400–1, the proposed NN-based algorithm achieves a fraction of misclassified neutron, gamma, and piled-up pulses of approximately 1%, 1.8%, and 0.6%, respectively. Compared to the traditional approach, it exhibits 3×, 14×, and 11× improved (lower) miscalculation rates for neutron, gamma, and piled-up pulses, respectively. Here, we also demonstrate the capability of NN-based algorithms of successfully recovering and identifying neutron and gamma ray compositions from piled-up pulses in challenging, high pulse count rate conditions.},
doi = {10.1016/j.anucene.2018.05.054},
journal = {Annals of Nuclear Energy (Oxford)},
number = ,
volume = 120,
place = {United States},
year = {Thu Jun 14 00:00:00 EDT 2018},
month = {Thu Jun 14 00:00:00 EDT 2018}
}
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https://doi.org/10.1016/j.anucene.2018.05.054
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