Automated Isotope Identification and Quantification Using Artificial Neural Networks
Citation:
Kamuda, Mark; Huff, Kathryn (2019): Automated Isotope Identification and Quantification Using Artificial Neural Networks. University of Illinois at Urbana-Champaign. https://doi.org/10.13012/B2IDB-4860767_V1
Persistent link for this dataset:
Related Code https://github.com/arfc/annsa
Dataset Description

This dataset contains gamma-ray spectra templates for a source interdiction and uranium enrichment measurement task. This dataset also contains Keras machine learning models trained using datasets created using these templates.
Subject Physical Sciences
Keywords gamma-ray spectroscopy; neural networks; machine learning; isotope identification; uranium enrichment; sodium iodide; NaI(Tl)
License CC BY
Funder U.S. Department of Energy (DOE) - Grant: DE-NA0002534
Corresponding Creator Kathryn Huff
Downloaded 2860 times
Version DOI Comment Publication Date
1 10.13012/B2IDB-4860767_V1 2019-12-12
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