Akademik Veri Yönetim Sistemi
INMM 66th Annual Meeting, Washington, Amerika Birleşik Devletleri, 25 - 28 Ağustos 2025, (Yayınlanmadı)
Nuclear smuggling and terrorism pose significant threats to national and global nuclear security, primarily due to the illicit trafficking of nuclear and radiological materials. To mitigate this, over 10,000 standard Radiation Portal Monitors (RPMs) have been deployed at borders, seaports, and customs worldwide. Additionally, Spectroscopic Radiation Portal Monitors (SRPMs) are increasingly used to improve detection capabilities during primary inspections. Recent efforts focus on enhancing RPMs to better distinguish threats from NORMs and reduce false or nuisance alarms. As part of these efforts, various radioisotope identification algorithms have been explored, including conventional methods (e.g., peak-based Bayesian, NLSQ, Fuzzy Logic) and modern approaches such as Artificial Convolutional Neural Networks (CNN). This study highlights the importance of SRPMs and the growing need for spectroscopic analysis capabilities. It proposes an alternative method to enable radionuclide identification in existing RPMs, focusing particularly on the photopeak-based Bayesian statistics approach. This enables for more effective detection of inadvertent movement and of Illicit Trafficking of Radioactive/Nuclear Materials.