  Static gamma-ray detector systems that are deployed outdoors for radiological
monitoring purposes experience time- and spatially-varying natural backgrounds
and encounters with man-made nuisance sources. In order to be sensitive to
illicit sources, such systems must be able to distinguish those sources from
benign variations due to, e.g., weather and human activity. In addition to
fluctuations due to non-threats, each detector has its own response and energy
resolution, so providing a large network of detectors with predetermined
background and source templates can be an onerous task. Instead, we propose
that static detectors use simple physics-informed algorithms to automatically
learn the background and nuisance source signatures, which can them be used to
bootstrap and feed into more complex algorithms. Specifically, we show that
non-negative matrix factorization (NMF) can be used to distinguish static
background from the effects of increased concentrations of radon progeny due to
rainfall. We also show that a simple process of using multiple gross count rate
filters can be used in real time to classify or ``triage'' spectra according to
whether they belong to static, rain, or anomalous categories for processing
with other algorithms. If a rain sensor is available, we propose a method to
incorporate that signal as well. Two clustering methods for anomalous spectra
are proposed, one using Kullback-Leibler divergence and the other using
regularized NMF, with the goal of finding clusters of similar spectral
anomalies that can be used to build anomaly templates. Finally we describe the
issues involved in the implementation of some of these algorithms on deployed
sensor nodes, including the need to monitor the background models for long-term
drifting due to physical changes in the environment or changes in detector
performance.
