Material Identification using Multi-Modal Intrinsic Radiation and Radiography

2026-03-27

AI Summary

This study addresses the challenge of accurately identifying special nuclear materials—such as beryllium-reflected plutonium spheres—encased in unknown shielding layers. The authors propose a multimodal approach that integrates X-ray imaging, high-resolution gamma-ray spectroscopy, and neutron multiplicity counting. For the first time, they jointly utilize net counts of gamma-ray spectral peaks and the neutron Feynman variance ratio (Y₂/Y₃) to construct multimodal features. Leveraging synthetic data generated by GADRAS, they train supervised classifiers, including random forest, to enable multi-class identification of shielding configurations. The method achieves near-perfect accuracy under single-layer shielding and significantly outperforms gamma-only approaches in more challenging double-layer scenarios. The work also includes a systematic evaluation of model mismatch effects on classification performance.

Abstract

We investigate multi-modal material identification for special nuclear material (SNM) configurations using a combination of X-ray radiography, high-resolution γ-ray spectroscopy, and neutron multiplicity measurements. We consider a Beryllium Reflected Plutonium sphere (BeRP) ball surrounded by one or two concentric shielding shells of unknown composition whose radii are assumed known from radiography. High-purity germanium (HPGe) spectra are reduced to net counts in selected Pu-239 photo-peaks, while neutron multiplicity information is summarized by Feynman variances Y2 and Y3 computed from factorial moments of the neutron counting statistics. Using synthetic data generated with the Gamma Detector Response and Analysis Software (GADRAS) for a range of shielding materials and thicknesses, we cast the material identification problem as a supervised multi-class classification task over all admissible shell-material combinations. We demonstrate that a random forest classifier trained on combined gamma and neutron features achieves almost perfect identification accuracy for single-shell cases, and substantial performance gains for more challenging double-shell configurations relative to gamma-only classification. Alternative statistical and machine-learning formulations for this multi-class problem are examined along with examination of the impact of model-mismatch between the forward model and the test cases as given by variations in the statistical noise. Opportunities for extending the approach to more complex geometries and experimental data are also discussed.

Problem

• material identification
• special nuclear material
• shielding characterization
• multi-modal sensing
• radiography

Innovation

• multi-modal material identification
• neutron multiplicity
• gamma-ray spectroscopy
• random forest classification
• special nuclear material