Simulation-Based Optimization of γ/γ Coincidence Spectrometry for the Radiological Characterization of Environmental Particulate Samples in Nuclear Monitoring

Radiological characterization of aerosols, whether for post-nuclear accident monitoring or under the Comprehensive Nuclear-Test-Ban Treaty (CTBT), remains challenging due to the simultaneous presence of numerous low-activity radionuclides and spectral interferences between their \(\:\gamma\:\)-rays in conventional \(\:\gamma\:\)-ray spectrometry. This study evaluates the contribution of a \(\:\gamma\:/\gamma\:\) coincidence spectrometry system, combined with Monte Carlo simulation-based efficiency calibration, to enhance radionuclide identification and activity quantification in scenarios where conventional \(\:\gamma\:\)-ray spectrometry is limited. To improve the accuracy of count estimation, particularly for low-level measurement, a Bayesian approach is adopted over the frequentist method. The results demonstrate that this approach enables both the confirmation of radionuclide presence and the estimation of their activities, with good agreement compared to reference values (average deviations below 15%). However, persistent discrepancies remain for X-ray photons, which are less accurately characterized due to the limited precision of models below 60 keV and greater uncertainties in their emission intensities. Collectively, these advancements position \(\:\gamma\:/\gamma\:\) coincidence spectrometry as a robust complementary method, significantly improving the reliability of radiological diagnosis in complex scenarios.