Detecting changes in maps of gamma spectra with Kolmogorov-Smirnov tests

TL;DR

This paper introduces a new spectral change detection method using Kolmogorov-Smirnov tests, improving continuous monitoring of radioactivity over large areas compared to existing source detection systems.

Contribution

The paper develops a novel change detection approach based on Kolmogorov-Smirnov tests and provides simulation tools for method comparison and deployment guidance.

Findings

The new method effectively detects sudden spectral changes in gamma-ray data.

Simulations show improved detection power over previous algorithms.

Visualizations assist in understanding method performance in different scenarios.

Abstract

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.