Data and software tools for gamma radiation spectral threat detection and nuclide identification algorithm development and evaluation

The detection of radiological and nuclear threats is extremely important to national security. The federal government is spending significant resources developing new detection systems and attempting to increase the performance of existing ones. The detection of illicit radionuclides that may pose a radiological or nuclear threat is a challenging problem complicated by benign radiation sources (e.g., cat litter and medical treatments), shielding, and large variations in background radiation. Although there is a growing acceptance within the community that concentrating efforts on algorithm development (independent of the specifics of fully assembled systems) has the potential for significant overall system performance gains, there are two major hindrances to advancements in gamma spectral analysis algorithms under the current paradigm: access to data and common performance metrics along with baseline performance measures. Because many of the signatures collected during performance measurement campaigns are classified, dissemination to algorithm developers is extremely limited. This leaves developers no choice but to collect their own data if they are lucky enough to have access to material and sensors. This is often combined with their own definition of metrics for measuring performance. These two conditions make it all but impossible for developers and external reviewers to make meaningful comparisons between algorithms. Without meaningful comparisons, performance advancements become very hard to achieve and (more importantly) recognize. The objective of this work is to overcome these obstacles by developing and freely distributing real and synthetically generated gamma-spectra data sets as well as software tools for performance evaluation with associated performance baselines to national labs, academic institutions, government agencies, and industry. At present, datasets for two tracks, or application domains, have been developed: one that includes temporal spectral data at 1 s time intervals, which represents data collected by a mobile system operating in a dynamic radiation background environment; and one that represents static measurements with a foreground spectrum (background plus source) and a background spectrum. These data include controlled variations in both Source Related Factors (nuclide, nuclide combinations, activities, distances, collection times, shielding configurations, and background spectra) and Detector Related Factors (currently only gain shifts, but resolution changes and non-linear energy calibration errors will be added soon). The software tools will allow the developer to evaluate the performance impact of each of these factors. Although this first implementation is somewhat limited in scope, considering only NaI-based detection systems and two application domains, it is hoped that (with community feedback) a wider range of detector types and applications will be included in the future. This article describes the methods used for dataset creation, the software validation/performance measurement tools, the performance metrics used, and examples of baseline performance.