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Overview of portal monitoring at border crossings

机译:边境口岸门户监控概述

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The Bureau of Customs and Border Protection has the task of interdicting illicit radioactive material at ports of entry. Items of concern include radiation dispersal devices (RDD), nuclear warheads, and special nuclear material (SNM). The preferred survey method screens all vehicles in primary and diverts questionable vehicles to secondary. This requires high detection probability in primary while not overwhelming secondary with alarms, which could include naturally occurring radioactive material (NORM) found in acceptable cargo and radionuclides used in medical procedures. Sensitive alarm algorithms must accommodate the baseline depression observed whenever a vehicle enters the portal. Energy-based algorithms can effectively use the crude energy information available from a plastic scintillator to distinguish NORM from SNM. Whenever NORM cargo limits the alarm threshold, energy-based algorithms produce significantly better detection probabilities for small SNM sources than gross-count algorithms. Algorithms can be best evaluated using a large empirical data set to 1) calculate false alarm probabilities, 2) select sigma-level thresholds for operationally acceptable false alarm rates, and 3) determine detection probabilities for marginally detectable pseudo sources of SNM.
机译:海关和边境保护局的任务是拦截入境口岸的非法放射性物质。令人关注的项目包括辐射扩散装置(RDD),核弹头和特殊核材料(SNM)。首选的调查方法会筛选主要车辆中的所有车辆,并将有问题的车辆转移至次要车辆。这就要求在初级时要有很高的检测概率,而在次级时又不能使警报不堪重负,这可能包括在可接受的货物中发现的天然放射性物质(NORM)和医疗程序中使用的放射性核素。敏感的警报算法必须适应每当车辆进入门户时观察到的基线下压。基于能量的算法可以有效地使用可从塑料闪烁体获得的原始能量信息来区分NORM和SNM。每当NORM货物限制警报阈值时,基于能量的算法对小型SNM源产生的检测概率要比总计数算法好得多。可以使用较大的经验数据对算法进行最佳评估,这些数据设置为:1)计算错误警报概率; 2)为操作可接受的错误警报率选择sigma级别阈值;以及3)确定SNM的边缘可检测伪源的检测概率。

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