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Tetris-inspired detector with neural network for radiation mapping.

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This study introduces a novel radiation mapping framework using Tetris-inspired detector pixels. This low-cost, high-resolution system effectively identifies radioactive sources for environmental monitoring.

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Area of Science:

  • Nuclear physics and instrumentation
  • Environmental science and monitoring
  • Computer science and machine learning

Background:

  • Radiation mapping is crucial for environmental monitoring but faces challenges in performance and cost.
  • Existing radiation detectors struggle with complex radiation-matter interactions and data limitations.
  • Developing high-resolution, low-cost radiation mapping solutions remains a significant research objective.

Purpose of the Study:

  • To present a novel radiation mapping framework utilizing Tetris-inspired detector pixels.
  • To demonstrate high-resolution directional prediction and accurate radiation source localization.
  • To offer a practical and cost-effective solution for real-world radiation detection applications.

Main Methods:

  • Development of a radiation mapping framework with Tetris-inspired detector pixels.
  • Application of inter-pixel padding to enhance contrast and neural networks trained with Monte Carlo (MC) simulation data for directional prediction.
  • Utilizing a moving detector with Maximum a Posteriori (MAP) for radiation position localization.

Main Results:

  • A detector with only four pixels achieved high-resolution directional prediction.
  • The Maximum a Posteriori (MAP) method enabled accurate radiation source localization.
  • Field testing validated the effectiveness of the MAP method for source localization.

Conclusions:

  • The proposed framework enables high-quality radiation mapping with simple detector configurations.
  • This approach offers a promising avenue for cost-effective and high-resolution radiation detection.
  • The system is anticipated for deployment in real-world environmental monitoring and radiation safety applications.