1Environmental and Radiological Health Sciences, 1618 Campus Delivery, Colorado State University, Fort Collins, CO 80523.
Environmental and Radiological Health Sciences, 1618 Campus Delivery, Colorado State University, Fort Collins, CO 80523.
Health Phys. 2019 May;116(5):727-735. doi: 10.1097/HP.0000000000001009.
The identification of radiological sources by analysis of a gamma spectrum usually relies on the location of the set of radionuclide-specific electron energies corresponding to the incident photons interacting by photoelectric absorption in the detection medium. The challenge in low-level detection applications is the identification of these "photopeaks" above the background counts registered in the detector from the natural radiation environment and system noise. For source detection decisions, regions of the gamma spectrum other than at the photopeak energies may provide additional information about the presence of a source and allow for a higher rate of correct identification of a weak source. A statistical algorithm utilizing low-fidelity spectral data partitioned into three distinct regions and employing a binomial discriminator was tested in a laboratory setting against the traditional approach of source identification by exceeding a decision threshold within the photopeak region of interest. For an unshielded Cs source with no significant scatter between the source and the detector, the traditional peak identification method performs as well or better than most algorithm settings for various source strengths. However, an algorithm which also includes information in the energy range of Compton scattered photons provides improved detection capabilities for shielded weak sources. Such algorithms, including higher-fidelity developments, could be deployed to improve current tools for the search for orphan radiological sources and in the characterization of low-level environmental contamination.
通过分析伽马能谱来识别放射性源，通常依赖于与入射光子相互作用的一组特定放射性核素的电子能量的位置，这些入射光子在探测介质中通过光电吸收相互作用。在低水平检测应用中，挑战在于在探测器中从自然辐射环境和系统噪声记录的本底计数中识别这些“光电峰”。对于源检测决策，除了光电峰能量之外，伽马光谱的其他区域可能会提供有关源存在的额外信息，并允许以更高的正确识别弱源的速率进行识别。在实验室环境中，使用一种利用低保真度光谱数据分成三个不同区域的统计算法，并使用二项式判别器，对传统的通过在感兴趣的光电峰区域内超过决策阈值来识别源的方法进行了测试。对于无屏蔽 Cs 源，在源和探测器之间没有明显散射的情况下，传统的峰识别方法的性能与各种源强度的大多数算法设置一样好，或者更好。然而，一种还包括康普顿散射光子能量范围内信息的算法，可以为屏蔽的弱源提供改进的检测能力。包括更高保真度开发的这种算法，可以部署到改进当前搜索孤儿放射性源的工具，并用于表征低水平环境污染物。
