METRA s.r.o.

Development and Validation of Advanced Neural Networks for Gamma-Ray Spectral Analysis

Gamma-ray spectroscopy is critical for applications ranging from nuclear facility monitoring to emergency response. However, traditional analysis methods struggle with overlapping peaks, noise, and complex spectra, often requiring expert intervention and achieving limited accuracy.

This research introduces an innovative approach leveraging specialized neural network architectures combining Convolutional Neural Networks (CNNs) with Transformer layers. Designed specifically for gamma-ray spectra, these networks integrate domain-specific constraints and advanced preprocessing techniques, enabling real-time, high-precision radioisotope identification.

Key achievements include:

  • Accuracy: 98.5% in isotope identification, a leap from the ~85% of traditional methods.
  • Speed: Real-time analysis (<10ms per spectrum) powered by GPU acceleration.
  • Robustness: Seamlessly handles overlapping peaks and complex spectral environments.
  • Field Validation: Demonstrated efficacy across scenarios, reducing operator analysis time by 30% and maintaining precision even in challenging cases.

The system employs cutting-edge features like adaptive wavelet-based denoising, physics-informed regularization, and a modular ensemble design for handling diverse spectral conditions. It also introduces real-time probabilistic metrics for improved decision-making in ambiguous scenarios.

This breakthrough represents a paradigm shift in gamma-ray spectroscopy, providing scalable, efficient, and highly accurate solutions for critical applications. Future developments aim to expand the approach to other radiation types and environmental conditions.

The project was implemented in cooperation with Couldera Technologies s.r.o. (report)

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