Encoding PGAA Spectra as Images for Material Classification with Convolutional Neural Networks

Published

May 27, 2025

Author(s)

Nathan Mahynski, David Sheen, Rick Paul, Huaiyu Chen-Mayer, Vincent Shen

Abstract

We trained deep convolutional neural networks (CNN) to classify a material based on its prompt gamma ray activation analysis (PGAA) spectrum. We focused on two dimensional (2D) models to leverage abundant open-source models pre-trained on other computer vision tasks for transfer learning. This allows models to be built with a relatively small number of trainable parameters. Moreover, CNNs can be explained naturally using class activation maps and can be equipped with out-of-distribution tests to identify materials which were not present in its training set. Together, these features suggest such models may be excellent candidates for automated material identification in real-world scenarios.

Citation

Journal of Radioanalytical and Nuclear Chemistry

Pub Type

Journals

Download Paper

https://doi.org/10.1007/s10967-025-10165-4

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Keywords

machine learning, non-targeted analysis, convolutional neural network, prompt gamma ray

Citation

Mahynski, N. , Sheen, D. , Paul, R. , Chen-Mayer, H. and Shen, V. (2025), Encoding PGAA Spectra as Images for Material Classification with Convolutional Neural Networks, Journal of Radioanalytical and Nuclear Chemistry, [online], https://doi.org/10.1007/s10967-025-10165-4, https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=959267 (Accessed June 5, 2025)