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Static gamma-ray detector systems that are deployed outdoors for radiologicalmonitoring purposes experience time- and spatially-varying natural backgroundsand encounters with man-made nuisance sources. In order to be sensitive toillicit sources, such systems must be able to distinguish those sources frombenign variations due to, e.g., weather and human activity. In addition tofluctuations due to non-threats, each detector has its own response and energyresolution, so providing a large network of detectors with predeterminedbackground and source templates can be an onerous task. Instead, we proposethat static detectors use simple physics-informed algorithms to automaticallylearn the background and nuisance source signatures, which can them be used tobootstrap and feed into more complex algorithms. Specifically, we show thatnon-negative matrix factorization (NMF) can be used to distinguish staticbackground from the effects of increased concentrations of radon progeny due torainfall. We also show that a simple process of using multiple gross count ratefilters can be used in real time to classify or ``triage'' spectra according towhether they belong to static, rain, or anomalous categories for processingwith other algorithms. If a rain sensor is available, we propose a method toincorporate that signal as well. Two clustering methods for anomalous spectraare proposed, one using Kullback-Leibler divergence and the other usingregularized NMF, with the goal of finding clusters of similar spectralanomalies that can be used to build anomaly templates. Finally we describe theissues involved in the implementation of some of these algorithms on deployedsensor nodes, including the need to monitor the background models for long-termdrifting due to physical changes in the environment or changes in detectorperformance.

Background and Anomaly Learning Methods for Static Gamma-ray Detectors