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Classification and Authentication of Materials using Prompt Gamma Ray Activation Analysis

Published

Author(s)

Nathan Mahynski, Jacob Monroe, David Sheen, Rick L. Paul, Heather H. Chen-Mayer, Vincent K. Shen

Abstract

Prompt gamma ray activation analysis (PGAA) is a non-destructive nuclear measurement technique that quantifies isotopes present in a sample. Here, we use PGAA spectra to train different types of models to elucidate how discriminating these spectra are for various classes of materials. We trained discriminative models for closed set scenarios, where all possible material classes are known. We also trained class models to address open set conditions, where this enumeration is impossible. After appropriate pre-processing and data treatments, all such models performed nearly perfectly on our dataset, suggesting PGAA spectra may serve as powerful nuclear fingerprints for robust material classification.
Citation
Journal of Radioanalytical and Nuclear Chemistry
Volume
332

Keywords

prompt gamma ray activation analysis, materials, machine learning, classification, authentication

Citation

Mahynski, N. , Monroe, J. , Sheen, D. , Paul, R. , Chen-Mayer, H. and Shen, V. (2023), Classification and Authentication of Materials using Prompt Gamma Ray Activation Analysis, Journal of Radioanalytical and Nuclear Chemistry, [online], https://doi.org/10.1007/s10967-023-09024-x, https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=936341 (Accessed June 23, 2024)

Issues

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Created July 12, 2023, Updated September 13, 2023