Graduate Thesis Or Dissertation
 

Special Nuclear Material Analysis Using Temporal Gamma-Ray Spectroscopy and Machine Learning Methods

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https://ir.library.oregonstate.edu/concern/graduate_thesis_or_dissertations/f7623h05t

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  • Following thermal neutron induced fission, supervised machine learning and temporal gamma-ray spectroscopy methods were used to identify differences in the delayed gamma-ray spectra of Pu-239 and U-235. The temporal gamma-ray spectroscopy method takes advantage of the time-dependent decay of fission products. Employing Spearman's rank-order correlation coefficient and without prior knowledge of peak locations or their associated energies, temporal behavior patterns characteristic of radioactive decay were identified within the complex fission product gamma-ray spectra below 3 MeV of Pu-239 and U-235. Individual rho (rho [subscript s]) values and their respective differences (Δ rho[subscript s]) were used as a part of feature selection to identify channels with significant attributes used as "fingerprints'' to create profiles for Pu-239 and U-235 which may be used for the identification of temporal behavior in an unknown sample. This method may be combined with additional machine learning techniques for future quantification of fissile material with the potential for similar accuracy and precision to previous temporal gamma-ray spectrometry methods.
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