Automated isotope identification algorithms for low-resolution gamma spectrometers

Gamma-ray spectroscopy;Bayesian classifiers;Isotope identification;Wavelets

Stinnett, Jacob Benjamin

关键词: Gamma-ray spectroscopy;    Bayesian classifiers;    Isotope identification;    Wavelets;   

Others  :  https://www.ideals.illinois.edu/bitstream/handle/2142/95304/STINNETT-DISSERTATION-2016.pdf?sequence=1&isAllowed=y

美国|英语

来源: The Illinois Digital Environment for Access to Learning and Scholarship

【 摘 要 】

Handheld radio-isotope identifiers (RIIDs) are widely deployed for nuclear security applications, but these detectors generally have poor isotope identification performance.Most of these deployed detectors use low-resolution NaI scintillators due to their low cost and good efficiency.Alternative detection hardware could be used to improve performance, but better detectors are generally cost-prohibitive for wide deployment in this mission space.However, a trained spectroscopist can use these low-resolution detectors to make much more accurate identifications than the RIIDs produce.For this reason, it has been suggested that these RIIDs could be significantly improved by changing the onboard identification algorithms. To this end, a peak-based Bayesian classifier has been developed to perform automated isotope identification.This algorithm was constructed to mimic the manual identification that a spectroscopist would perform.This approach can handle challenges such as detector calibration drift and unknown shielding scenarios, is capable of identifying mixed radiation sources, and is computationally inexpensive enough to be feasible for deployment on handheld RIID systems.A method for easily generating isotope libraries that are coupled to the detector and to the feature extraction algorithm is presented as well.This method is demonstrated on a broad variety of gamma-ray spectra, ranging from small calibration sources ($