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NeutralNet: Development and testing of a machine learning solution for pulse shape discrimination.

Richard L Garnett1, Soo Hyun Byun1

  • 1Department of Physics and Astronomy, McMaster University, Hamilton, ON, L8S 4K1, Canada.

Applied Radiation and Isotopes : Including Data, Instrumentation and Methods for Use in Agriculture, Industry and Medicine
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Summary

Machine learning, specifically a modified GoogLeNet, significantly improved neutron detection using liquid scintillators. This method achieved high accuracy in distinguishing neutrons from photons in mixed radiation fields.

Keywords:
Machine learningNeutron gamma separationPulse shape discrimination

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Area of Science:

  • Nuclear Instrumentation and Measurement
  • Applied Machine Learning
  • Radiation Detection Physics

Background:

  • Characterizing neutron radiation fields is challenging due to detector sensitivity and secondary particle generation in mixed environments.
  • Pulse shape discrimination (PSD) in liquid scintillators is crucial for identifying neutrons amidst other radiation types.

Purpose of the Study:

  • To develop and evaluate machine learning architectures for enhanced pulse shape discrimination in liquid scintillators.
  • To investigate the impact of digitizer sampling parameters on machine learning algorithm performance for neutron detection.

Main Methods:

  • Utilized EJ-301 liquid scintillator and a CAEN DT-5743 digitizer (3.2 GHz, 12-bit resolution).
  • Employed 238Pu9Be and 241Am9Be neutron sources and 24Na, 60Co, 137Cs photon sources for data generation.
  • Tested various machine learning architectures, including a modified GoogLeNet, with varying digitizer sampling rates and bit depths.

Main Results:

  • A modified GoogLeNet architecture achieved the highest performance, with a 69.17% true positive rate for neutron identification.
  • Achieved exceptional photon rejection rate of 99.9999%.
  • Performance varied with neutron energy, ranging from 1.30% at >3 MeVee to 89.96% between 340-1000 keVee, with 21.48% identification below 200 keVee.

Conclusions:

  • Machine learning, particularly the optimized GoogLeNet, significantly enhances neutron detection capabilities in mixed radiation fields.
  • Full digitizer sampling rate and bit depth are critical for achieving optimal performance in machine learning-based PSD.
  • The developed method shows promise for accurate neutron field characterization, especially in lower energy ranges.