Material Identification using Multi-Modal Intrinsic Radiation and Radiography

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.

Figures (9)

• Figure 1: Feynman Variance $Y_2$ and $Y_3$ for BeRP ball versus shielding shell thickness
• Figure 2: General problem formulation.
• Figure 3: Classification errors by material and thickness in single shell configuration using Gamma+$Y_2$+$Y_3$ as feature.
• Figure 4: Normalized confusion matrices for single-shell material identification at two shell thicknesses (rows) and three feature sets (columns): Gamma only (left), Gamma + $Y_2$ (middle), and Gamma + $Y_2$ + $Y_3$ (right), using 60s time window, clean data for training, and $N=10{,}000$ independent noise realizations per feature set and thickness for testing. Each panel shows the classification performance across all candidate shell materials under noisy measurement conditions. We normalize these matrices so that the sum of classification errors for each reference material is 1 (if they are non-zeros).
• Figure 5: Misclassification fractions for representative two-shell configurations (rows) under three feature sets (columns): Gamma only (left), Gamma + $Y_2$ (middle), and Gamma + $Y_2$ + $Y_3$ (right) using 1800s time window and clean data for training. $N=10{,}000$ independent noise realizations per feature set and thickness are generated for testing. Each panel summarizes the fraction of test cases that are misclassified for the specified shell-thickness configuration (inner/outer).