DOE OSTI · 2568417
A Fast Framework for Generating Radioactive Mixture Spectra and Its Application to Remote High-Performance Mixture Identification
Abstract
Remote detection of radioactive materials in mixtures using handheld or portal detectors remains a challenge because of factors such as low concentration, environmental interference, sensor noise, and other complications. This work introduces a fast framework for generating realistic mixture spectra. Moreover, we present mixture isotope identification using data generated by the fast framework. Researchers have examined a range of conventional and recent algorithms within the fields of machine learning and deep learning. An application to uranium enrichment-level prediction has been included. Extensive simulation experiments validated the efficacy of the proposed framework.
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Kwan, Chiman [Applied Research LLC, Rockville, MD (United States)] (ORCID:0000000243410769), Ayhan, Bulent [Applied Research LLC, Rockville, MD (United States)], Stavola, Adam [Thomas Jefferson National Accelerator Facility (TJNAF), Newport News, VA (United States)], Islam, Kazi Aminul [Kennesaw State University, GA (United States)] (ORCID:0000000293200858), Zhang, Hongfang [Old Dominion Univ., Norfolk, VA (United States)] (ORCID:0009000689244192), Li, Jiang [Old Dominion Univ., Norfolk, VA (United States)] (ORCID:0000000300916986). 2025-04-21. A Fast Framework for Generating Radioactive Mixture Spectra and Its Application to Remote High-Performance Mixture Identification. https://doi.org/10.3390/electronics14081688
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