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Pseudo-Gamma Spectroscopy Based on Plastic Scintillation Detectors Using Multitask Learning.

Byoungil Jeon1,2, Junha Kim3, Eunjoong Lee4

  • 1Applied Artificial Intelligence Laboratory, Korea Atomic Energy Research Institute, Daejeon 34507, Korea.

Sensors (Basel, Switzerland)
|January 27, 2021
PubMed
Summary

This study introduces a deep learning model for pseudo-gamma spectroscopy using plastic scintillation detectors. The model accurately identifies radioisotopes and predicts radioactivity, even with limited data.

Keywords:
deep learningfull-energy peak unfoldingmultitask modelphotopeakplastic gamma spectrumrelative radioactivity prediction

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Area of Science:

  • Nuclear Physics
  • Spectroscopy
  • Machine Learning

Background:

  • Plastic scintillation detectors are widely used for radiation measurement but have poor spectroscopic capabilities.
  • Existing methods for spectroscopic analysis with plastic scintillators often only identify radioisotopes.
  • Improving spectroscopic analysis of plastic scintillation detectors is crucial for advanced radiation measurement applications.

Purpose of the Study:

  • To develop a multitask deep learning model for pseudo-gamma spectroscopy using plastic scintillation detectors.
  • To enhance the spectroscopic performance of plastic scintillation detectors for accurate radioisotope identification and radioactivity prediction.
  • To address the limitations of current spectroscopic methods in plastic scintillation detection.

Main Methods:

  • A deep learning model utilizing multitask learning and supervised learning was implemented.
  • Spectra were generated using Monte Carlo N-Particle (MCNP 6.2) simulations and polyvinyl toluene detector measurements.
  • A weighted multi-head self-attention module was integrated to improve model performance.
  • Hyperparameter tuning was performed using Bayesian optimization.

Main Results:

  • The proposed model successfully unfolds full-energy peaks from gamma-ray spectra.
  • The model accurately predicts the relative radioactivity of single and multiple gamma-ray sources.
  • Performance was validated using measured plastic gamma spectra and a defined minimum required count metric.
  • The model demonstrated effectiveness even in spectra with statistical uncertainties.

Conclusions:

  • The developed multitask deep learning model significantly enhances pseudo-gamma spectroscopy capabilities for plastic scintillation detectors.
  • This approach overcomes the inherent spectroscopic limitations of plastic scintillators, enabling more precise radiation analysis.
  • The model offers a robust solution for radioisotope identification and radioactivity quantification in various radiation measurement scenarios.