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

Fu, C.; Di Fulvio, A.; Clarke, S. D.; Wentzloff, D.; Pozzi, S. A.; Kim, H. S.

doi:10.1016/j.anucene.2018.05.054

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

Journal Article · Thu Jun 14 00:00:00 EDT 2018 · Annals of Nuclear Energy (Oxford)

DOI:https://doi.org/10.1016/j.anucene.2018.05.054· OSTI ID:1798642

Fu, C. ^[1]; Di Fulvio, A. ^[1]; Clarke, S. D. ^[1]; Wentzloff, D. ^[1]; Pozzi, S. A. ^[1]; Kim, H. S. ^[1]

Univ. of Michigan, Ann Arbor, MI (United States)

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.

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Research Organization:: Univ. of Michigan, Ann Arbor, MI (United States)

Sponsoring Organization:: USDOE National Nuclear Security Administration (NNSA)

Grant/Contract Number:: NA0002534

OSTI ID:: 1798642

Alternate ID(s):: OSTI ID: 1582705

Journal Information:: Annals of Nuclear Energy (Oxford), Vol. 120; ISSN 0306-4549

Publisher:: ElsevierCopyright Statement

Country of Publication:: United States

Language:: English

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Cited by: 38 works

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