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Implementation of machine learning algorithms for detecting missing radioactive material

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Abstract

The detection of missing radioactive material is an important capability for safeguards measurements. Gamma ray signatures provide sample information, but interpretation is complicated by measurement environments. To determine if machine learning is a viable analysis option, three algorithms are applied to gamma ray detection data to assess their success at identifying missing sources. Preliminary results demonstrate that these algorithms can predict the number and location of missing sources on simple models of spent fuel assemblies. In addition to simulated experiments, a study to investigate if the algorithms can be trained with simulated data and tested on measured data is presented.

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Acknowledgements

This work was supported in part by the U.S. Nuclear Regulatory Commission’s Nuclear Education Program Fellowship Grant Program.

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  1. Ken and Mary Alice Lindquist, Department of Nuclear Engineering, The Pennsylvania State University, 205 Hallowell Building, University Park, PA, 16801, USA

    Matthew Durbin & Azaree Lintereur

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  1. Matthew Durbin
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  2. Azaree Lintereur
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Correspondence to Matthew Durbin.

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Durbin, M., Lintereur, A. Implementation of machine learning algorithms for detecting missing radioactive material. J Radioanal Nucl Chem 324, 1455–1461 (2020). https://doi.org/10.1007/s10967-020-07188-4

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  • DOI: https://doi.org/10.1007/s10967-020-07188-4

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