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<p>Increasing the fidelity of analyses of nuclear material is an important goal of technical nuclear forensics R&amp;D. Many well-proven and mature analysis tools exist; some give visual representations of materials, while others offer clues to the chemical composition. Two such tools are Scanning Electron Microscopy (SEM) and X-ray diffraction (XRD). We present a machine learning framework for fusing these distinct modalities—themselves well-studied tools of nuclear forensics—to increase their specificity together. Early model fusion allows the use of correlations between data sources. We significantly improve accuracy over any single model alone, even over baseline methods that incorporate ground-truth information about the material. If XRD data is limited, we show that generating synthetic patterns from crystallographic information is viable for increasing available data. Comparing models trained on real and synthetic XRD pattern data, we conclude that our early fusion method trained on real XRD pattern data can utilize processing route information previously hidden in the XRD pattern, outperforming late fusion methods and models trained on synthetic data alone.</p>

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An exploration of data fusion techniques applied to nuclear forensics tasks

  • 期刊论文

  • 发表日期：

    2025年11月11日

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  • Jakob Johnson,
  • Luther McDonald,
  • Tolga Tasdizen

摘要

Increasing the fidelity of analyses of nuclear material is an important goal of technical nuclear forensics R&D. Many well-proven and mature analysis tools exist; some give visual representations of materials, while others offer clues to the chemical composition. Two such tools are Scanning Electron Microscopy (SEM) and X-ray diffraction (XRD). We present a machine learning framework for fusing these distinct modalities—themselves well-studied tools of nuclear forensics—to increase their specificity together. Early model fusion allows the use of correlations between data sources. We significantly improve accuracy over any single model alone, even over baseline methods that incorporate ground-truth information about the material. If XRD data is limited, we show that generating synthetic patterns from crystallographic information is viable for increasing available data. Comparing models trained on real and synthetic XRD pattern data, we conclude that our early fusion method trained on real XRD pattern data can utilize processing route information previously hidden in the XRD pattern, outperforming late fusion methods and models trained on synthetic data alone.