Application of Artificial Neural Networks to Elemental Assay Data for Nuclear Forensics Analysis

Abstract

An Artificial Neural Network (ANN) is applied to elemental assay data of microscopic, actinide bearing particles obtained using energy dispersive x-ray spectroscopy via a scanning electron microscope (SEM-EDS) and Electron ProbeMicro Analysis (EPMA). This technique provides a non-destructive assessment of the composition of particles that is suitable for nuclear forensics applications. A moment transformation was applied to the data before the ANN was used to compare and group like-particles together using a Siamese network and triplet loss function. A moment transformation provided a noticeable increase in accuracy across all runs. Models using triplet loss had nearly perfect precision when two observations were the same, and provided a preliminary means to compare unknown samples to a database of known samples. Adjusting the hyper parameters could further increase the performance of the models.

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Document Details

Document Type
Technical Report
Publication Date
Mar 01, 2021
Accession Number
AD1145746

Entities

People

  • Jason G. Seik

Organizations

  • Air Force Institute of Technology

Tags

Communities of Interest

  • Autonomy
  • Energy and Power Technologies

DTIC Thesaurus Topics

  • Accuracy
  • Actinides
  • Air Force
  • Databases
  • Dimensionality Reduction
  • Electron Microscopes
  • Electron Probes
  • Engineering
  • Information Science
  • Machine Learning
  • Neural Networks
  • Nuclear Forensics
  • Scanning Electron Microscopes
  • Spectroscopy
  • United States Government
  • X Ray Spectroscopy
  • X Rays

Readers

  • Neural Network Machine Learning.
  • Quantum spin resonance or Electron Paramagnetic Resonance spectroscopy.
  • Thin Film Deposition Science.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference
  • AI & ML - Neural Networks
  • Microelectronics