Akademik Veri Yönetim Sistemi
Nuclear Engineering and Technology, cilt.58, ss.1-9, 2026 (SCI-Expanded, Scopus)
Radioisotope identification devices(RID) play a crucial role in
detection and identification of illicit trafficking of
radioactive/nuclear materials in nuclear security and nuclear
safeguards. These devices utilize various algorithms for automated
isotope identification(ID) without the need for expert intervention. In
this study, an automated algorithm for real-time isotope identification
is presented. The algorithm employs a second-derivative-based peak
detection and a Bayesian-statistics-peak based ID approach. To
demonstrate the suitability of the developed algorithm, it was applied
to the gamma-ray spectra acquired with a medium energy -resolution LaBr3(Ce) detector. In addition to point sources 60Co, 109Cd, 22Na, 137Cs, 241Am, 152Eu, and 133Ba,
the algorithm was also tested on the more complex gamma-ray spectra
obtained from low enriched uranium reference materials 171 (EC-NRM171),
and natural uranium and thorium minerals such as BL-2, BL-3, BL-4A,
BL-5, RGU and OKA-2. To evaluate the performance of the algorithm, total
scoring (ST) is calculated. For identification of 22Na, 60Co, and 137Cs isotopes, the posterior probabilities were estimated to be greater than 99 %. For 133Ba, 152Eu, and 241Am,
the isotopes they were also correctly identified with higher posterior
probabilities ranged from 92 % to 95 %. The developed algorithm
successfully identified the isotopes contained in U-Th ore samples with a
100 % total score. Additionally, the performance evaluation of the
results obtained with Certified Reference Uranium Materials also
demonstrated 100 % score. For automatic ID, the photopeak-based Bayesian
method, combined with the Mariscotti's peak detection method has great
potential for real-time ID when implemented in RID devices.