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<p>Mobile gamma spectrometry is crucial for detecting gamma-ray sources in environmental monitoring and homeland security. The Maximum Detectable Distance (MDD) defines the farthest reliable detection range; however, conventional models fail to account for spatial variations in radiation intensity during movement. This study introduces a Physics-Informed Neural Network (PINN) to refine MDD predictions by incorporating detector speed, angular efficiency, and detection probability. For an unshielded <sup>137</sup>Cs source with 95% detection probability, the optimal MDD of 5.4&#xa0;m aligns with theoretical expectations. By dynamically adjusting for speed and angular effects, this framework enhances real-time predictions, improving field survey efficiency and remediation strategies.</p>

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Maximum detectable distance in mobile gamma spectrometry using physics-informed neural network: optimizing radiation detection

  • 期刊论文

  • 发表日期：

    2025年5月21日

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  • Nancy A. Ibrahim,
  • Amin Amirlatifi,
  • Peixue Ma,
  • Somayeh Bakhtiari Ramezani

摘要

Mobile gamma spectrometry is crucial for detecting gamma-ray sources in environmental monitoring and homeland security. The Maximum Detectable Distance (MDD) defines the farthest reliable detection range; however, conventional models fail to account for spatial variations in radiation intensity during movement. This study introduces a Physics-Informed Neural Network (PINN) to refine MDD predictions by incorporating detector speed, angular efficiency, and detection probability. For an unshielded 137Cs source with 95% detection probability, the optimal MDD of 5.4 m aligns with theoretical expectations. By dynamically adjusting for speed and angular effects, this framework enhances real-time predictions, improving field survey efficiency and remediation strategies.