The rapid identification of \(\gamma\) -emitting radionuclides with low activity levels in public areas is crucial for nuclear safety. However, classical methods rely on full-energy peaks in the integral spectrum, requiring sufficient count accumulation for evaluation, thereby limiting response time. The sequential Bayesian approach, which utilizes prior information and considers both photon energies and interarrival times, can significantly enhance the performance of radionuclides identification. This study proposes a theoretical optimization method for the traditional sequential Bayesian approach. Each photon is processed sequentially, and the corresponding posterior probability is updated in real time using a noninformative prior from the Bayesian theory. By comparing the posterior probabilities of the background and radionuclides based on the energy variance and time interval, the type of \(\gamma\) -rays can be identified (background characteristic \(\gamma\) -rays, Compton plateaus \(\gamma\) -rays, or radionuclide-specific characteristic \(\gamma\) -rays). By integrating the information from these multiple characteristic \(\gamma\) -rays, the presence and type of radionuclides were determined based on the final decision function and a set threshold. Based on theoretical research, verification experiments were conducted using a \(\hbox {LaBr}_3\) (Ce) detector in both low-and natural background radiation environments with typical radionuclides (137Cs, 60Co, and 133Ba). The results show that this approach can identify 137Cs in 7.9 s and 8.5 s (source dose rate contribution: approximately \(6.5\times 10^{-3}\) μGy/h), 60Co in 8.1 s and 9.8 s (approximately \(4.8\times 10^{-2}\) μGy/h), and 133Ba in 4.05 s and 5.99 s (approximately \(3.4\times 10^{-2}\) μGy/h) under low and natural background radiation, respectively, with a miss rate below \(0.01 \%\) . This demonstrates the effectiveness of the proposed approach for fast radionuclides identification, even at low activity levels and highlights its potential for enhancing public safety in diverse radiation environments.
