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

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

Research output: Contribution to journalArticlepeer-review

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. 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.

Original languageEnglish (US)
Pages (from-to)410-421
Number of pages12
JournalAnnals of Nuclear Energy
Volume120
DOIs
StatePublished - Oct 2018
Externally publishedYes

Keywords

  • Neural networks
  • Organic scintillators
  • Piled-up identification
  • Pulse shape discrimination

ASJC Scopus subject areas

  • Nuclear Energy and Engineering

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