1Colorado State University, Fort Collins, CO.
Colorado State University, Fort Collins, CO.
Health Phys. 2019 Jul;117(1):28-35. doi: 10.1097/HP.0000000000001046.
Operational health physics applications, such as radiological and nuclear monitoring and detection for homeland security or radiation protection purposes, generate time sequences of independent individual measurement data. Statistical algorithms have been developed that use the analysis of patterns in the data strings to enhance the test statistic for the decision on the absence or presence of a radiation source. These hypothesis test procedures have been applied to spectral data and have been optimized for the highest rate of correct identification of a weak Cs source at constant false positive detection rates. Optimization of correct detection decisions was investigated for various string data sequence lengths and for the regions of interest in the gamma spectrum. The highest correct source identification is achieved for string data analyses of the spectral contributions that maximize a [INCREMENT]μ/σ criterion, including energy regions around and containing the photopeak, but potentially also regions in the gamma spectrum other than those photopeak energies.
操作保健物理应用，如国土安全或辐射防护目的的放射性和核监测和探测，会产生独立的个体测量数据的时间序列。已经开发了统计算法，该算法使用对数据字符串中的模式的分析来增强用于确定是否存在辐射源的测试统计信息。这些假设检验程序已应用于光谱数据，并针对在恒定假阳性检测率下正确识别弱 Cs 源的最高速率进行了优化。针对不同的字符串数据序列长度以及伽马谱中的感兴趣区域，研究了正确检测决策的优化。对于最大化 [增量] μ/σ 准则的光谱贡献的字符串数据分析，可以实现最高的正确源识别，包括包含和不包含光峰的能区，但也可能包括那些光峰能量之外的伽马谱区域。
