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Updated: Sep 22, 2025

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Thermal neutron beam optimization for PGNAA applications using Q-learning algorithm and neural network.

Mona Zolfaghari1, S Farhad Masoudi2, Faezeh Rahmani1

  • 1Department of Physics, K.N. Toosi University of Technology, P.O. Box 15875-4416, Tehran, Iran.

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|May 23, 2022
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Prompt gamma neutron activation analysis (PGNAA) requires thermalized neutrons. This study optimizes thermalization devices (TDs) using a neural network and Q-learning, significantly reducing simulation time for efficient PGNAA neutron sources.

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Area of Science:

  • Nuclear Physics
  • Materials Science
  • Computational Physics

Background:

  • Prompt gamma neutron activation analysis (PGNAA) is a non-destructive technique.
  • PGNAA relies on thermal neutron capture reactions, necessitating thermalized neutron sources.
  • Optimizing thermalization devices (TDs) for electron Linac-based neutron sources is crucial for maximizing thermal neutron flux.

Purpose of the Study:

  • To develop an efficient method for optimizing the geometry of thermalization devices (TDs).
  • To reduce the time-consuming nature of multi-parameter optimization using traditional Monte Carlo methods.
  • To enhance thermal neutron flux for PGNAA applications.

Main Methods:

  • Utilized a multilayer perceptron (MLP) neural network in conjunction with a Q-learning algorithm.
  • Optimized collimator thickness and diameter using MLP for various electron energies and moderator configurations.
  • Employed MCNPX2.6 code for calculating neutron flux and training the MLP model.

Main Results:

  • Successfully optimized collimator geometry (thickness and diameter) using MLP.
  • Determined optimal moderator thicknesses for different Linac electron energies via Q-learning and MLP.
  • Achieved optimal TD setup with significantly fewer simulations compared to conventional Monte Carlo methods.

Conclusions:

  • The combined MLP and Q-learning approach provides an efficient and faster method for TD optimization.
  • This AI-driven optimization strategy accelerates the development of suitable neutron sources for PGNAA.
  • The study demonstrates a significant reduction in computational cost for complex optimization problems in nuclear analysis.