Speaker
Abigail Taylor
(Georgia Institute of Technology)
Summary
This work investigates radioisotope identification with gamma ray spectroscopy using three detector types—NaI(Tl), CZT, and HPGe—for the characterization of both simple and complex irradiated materials. Instead of traditional peak-based nuclide identification, Machine Learning (ML) classification algorithms are employed to enable faster and more robust isotope recognition. Training models on simplified spectra enhances classification accuracy when applied to complex mixtures.
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Author
Abigail Taylor
(Georgia Institute of Technology)
Co-authors
Dr
Steven Biegalski
(Georgia Institute of Technology)
Dr
Derek Haas
(University of Texas at Austin)
Dr
Khiloni Shah
(University of Texas at Austin)
Kohlton Mathis
(University of Texas at Austin)
Joseph Lapka
(University of Texas at Austin)
Sarah Castro
(University of Texas at Austin)
