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<p>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.</p>

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Encoding PGAA spectra as images for material classification with convolutional neural networks

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年5月27日

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

摘要

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.