Bayesian Analysis of Energy and Count Rate Data for Detection of low Count Rate Radioactive Sources


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Abstract

A particle counting and detection system is proposed that searches for elevated count rates in multiple energy regions simultaneously. The system analyzes time-interval data (e.g., time between counts), as this was shown to be a more sensitive technique for detecting low count rate sources compared to analyzing counts per unit interval (Luo et al. 2013). Two distinct versions of the detection system are developed. The first is intended for situations in which the sample is fixed and can be measured for an unlimited amount of time. The second version is intended to detect sources that are physically moving relative to the detector, such as a truck moving past a fixed roadside detector or a waste storage facility under an airplane. In both cases, the detection system is expected to be active indefinitely; i.e., it is an online detection system. Both versions of the multi-energy detection systems are compared to their respective gross count rate detection systems in terms of Type I and Type II error rates and sensitivity.

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