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Multi-radioisotope identification algorithm using an artificial neural network for plastic gamma spectra.

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Applied Radiation and Isotopes : Including Data, Instrumentation and Methods for Use in Agriculture, Industry and Medicine
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This study introduces an artificial neural network (ANN) algorithm for accurate radioisotope identification using plastic scintillators. The novel method achieves high accuracy for both single and multiple radioisotope detection, overcoming detector limitations.

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Area of Science:

  • Nuclear Physics
  • Spectroscopy
  • Machine Learning

Background:

  • Plastic scintillation detectors offer advantages in certain applications but suffer from poor spectral resolution and low cross-sections for photoelectric absorption, complicating radioisotope identification.
  • Accurate radioisotope identification is crucial for nuclear security, environmental monitoring, and medical applications.

Purpose of the Study:

  • To develop and validate an artificial neural network (ANN)-based algorithm for identifying single and multiple radioisotopes from gamma-ray spectra acquired using plastic scintillators.
  • To assess the accuracy and robustness of the proposed ANN algorithm under various conditions, including spectral shifts.

Main Methods:

  • Gamma-ray spectra were simulated using the Monte Carlo N-Particle Transport Code 6 (MCNP6) to generate a comprehensive training dataset.
  • Experimental spectra were acquired using a two-inch EJ-200 plastic scintillator, forming a test set of 1440 measurements.
  • An artificial neural network was trained on the simulated data and tested on the experimental data for radioisotope identification.

Main Results:

  • The ANN-based algorithm achieved a high identification accuracy of 98.9% for single radioisotopes and 99.1% for multiple radioisotopes.
  • The algorithm demonstrated robustness, maintaining high accuracy even when spectra were intentionally shifted by 36 keV at low and high energies.
  • The minimum number of detected counts required for radioisotope identification with a 5% false negative and false positive rate was determined.

Conclusions:

  • The proposed ANN algorithm significantly enhances the capability of plastic scintillation detectors for accurate radioisotope identification.
  • This method offers a reliable solution for complex radioisotope identification tasks, even with challenging spectral data.
  • The findings provide a foundation for improved real-time radioisotope detection systems in various fields.