Fast nuclide identification based on a sequential Bayesian method

The rapid identification of radioactive substances in public areas is crucial. However, traditional nuclide identification methods only consider information regarding the full energy peaks of the gamma-ray spectrum and require long recording times, which lead to long response times. In this paper, a novel identification method using the event mode sequence（EMS） information of target radionuclides is proposed. The EMS of a target radionuclide and natural background radiation were established as two different probabilistic models and a decision function based on Bayesian inference and sequential testing was constructed. The proposed detection scheme individually processes each photon. When a photon is detected and accepted, the corresponding posterior probability distribution parameters are estimated using Bayesian inference and the decision function is updated.Then, value of the decision function is compared to preset detection thresholds to obtain a detection result. Experiments on different target radionuclides(Cs andCo)were performed. The count rates of the regions of interest（ROI） in the backgrounds between [651, 671], [1154,1186], and [1310, 1350] keV were 2.35, 5.14, and 0.57 CPS, respectively. The experimental results demonstrate that the average detection time was 6.0 s forCo（with an activity of 80400 Bq） at a distance of 60 cm from the detector. The average detection time was 7 s forCs（with an activity of 131000 Bq） at a distance of 90 cm from the detector. The results demonstrate that the proposed method can detect radioactive substances with low activity.