  Various security, regulatory, and consequence management agencies are
interested in continuously monitoring wide areas for unexpected changes in
radioactivity. Existing detection systems are designed to search for
radioactive sources but are not suited to repeat mapping and change detection.
Using a set of daily spectral observations collected at the Pickle Research
Campus, we improved on the prior Spectral Comparison Ratio Anomaly Mapping
(SCRAM) algorithm and developed a new method based on two-sample
Kolmogorov-Smirnov tests to detect sudden spectral changes. We also designed
simulations and visualizations of statistical power to compare methods and
guide deployment scenarios.
