Radiation anomaly detection and classification with Bayes Model Selection

Abstract

We present a new method for radiation anomaly detection that is based on Bayes Model Selection (BMS), together with models for gamma-radiation measurements from benign and threat sources. The method estimates the relative odds of pairs of such models, with the aim of supporting related hypotheses about the nature of the underlying source material. We also discuss partial optimization of the parameters in the models. The method allows measurements to be broadly categorized and screened for sources of interest in real time, a property that should improve the efficiency of mobile search or unattended monitoring operations