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.

Spectroscopic anomaly detection and isotope identification algorithms are integral components in nuclear nonproliferation applications such as search operations. The task is especially challenging in the case of mobile detector systems due to the fact that the observed gamma-ray background changes more than for a static detector system, and a pretrained background model can easily find itself out of domain. The result is that algorithms may exceed their intended false alarm rate, or sacrifice detection sensitivity in order to maintain the desired false alarm rate. Non-negative matrix factorization (NMF) has been shown to be a powerful tool for spectral anomaly detection and identification, but, like many similar algorithms that rely on data-driven background models, in its conventional implementation it is unable to update in real time to account for environmental changes that affect the background spectroscopic signature. We have developed a novel NMF-based algorithm that periodically updates its background model to accommodate changing environmental conditions. The Adaptive NMF algorithm involves fewer assumptions about its environment, making it more generalizable than existing NMF-based methods while maintaining or exceeding detection performance on simulated and real-world datasets.

Static and mobile radiation detectors can be deployed in urban environments for a range of nuclear security applications, including radiological source search-and-tracking scenarios. Modeling detector performance for such applications is challenging, as it does not depend solely on the detector capabilities themselves. Many factors must be taken into consideration, including specific source and background signatures, the topology and constraints of the deployment environment, the presence of nuisance sources, and whether detectors are mobile or static. When considering the simultaneous deployment of multiple, heterogeneous detectors, assessment of the system-wide performance requires the simulation of the individual detectors, and a system-level analysis of the detection performance. In radiological source search-and-tracking scenarios, performance is mostly dominated by the probability of encounter, which depends on the specifics of a given deployment, e.g., static vs. mobile detectors or a combination of both modalities, the number of detectors deployed, the dynamic vs. static setting of false alarm rates, and individual vs. networked operation. The Urban Deployment Model (UDM) toolset was specifically developed to cover the gap in the available generic frameworks for the simulation of radiation detector deployments at city scales. UDM provides a unified and modular framework to support the simulation and performance characterization of heterogeneous detector deployments in urban environments. This paper presents the key components along the UDM workflow.

The yields of $$K^0_S$$K S 0 mesons have been measured in inelastic p+p interactions at incident projectile momenta of 31, 40 and 80 $$\text {Ge}\hspace{-1.00006pt}\text {V}\!/\!c$$Ge V / c ( $$\sqrt{s_{NN}}=7.7, 8.8$$s NN = 7.7 , 8.8 and 12.3 $$\text {Ge}\hspace{-1.00006pt}\text {V}$$Ge V , respectively). The data were recorded by the NA61 $$/$$/ SHINE spectrometer at the CERN Super Proton Synchrotron and the $$K^0_S$$K S 0 mesons identified via their decays into $$\pi ^{+} \pi ^{-}$$π + π - pairs. Double-differential distributions are presented as function of transverse momentum and rapidity. The mean multiplicities of $$K^0_S$$K S 0 mesons were determined to be $$(5.95 \pm 0.19 (stat) \pm 0.30 (sys)) \times 10^{-2}$$( 5.95 ± 0.19 ( s t a t ) ± 0.30 ( s y s ) ) × 10 - 2 at 31 $$\text {Ge}\hspace{-1.00006pt}\text {V}\!/\!c$$Ge V / c , $$(7.61 \pm 0.13 (stat) \pm 0.43 (sys)) \times 10^{-2}$$( 7.61 ± 0.13 ( s t a t ) ± 0.43 ( s y s ) ) × 10 - 2 at 40 $$\text {Ge}\hspace{-1.00006pt}\text {V}\!/\!c$$Ge V / c and $$(11.58 \pm 0.12 (stat) \pm 0.55 (sys)) \times 10^{-2}$$( 11.58 ± 0.12 ( s t a t ) ± 0.55 ( s y s ) ) × 10 - 2 at 80 $$\text {Ge}\hspace{-1.00006pt}\text {V}\!/\!c$$Ge V / c . The results on $$K^{0}_{S}$$K S 0 production are compared with the production of charged kaons in corresponding reactions and with model calculations (Epos1.99, SMASH 2.0 and PHSD) as well as with published data from other experiments.

Sparse static detector networks in urban environments can be used in efforts to detect illicit radioactive sources, such as stolen nuclear material or radioactive "dirty bombs". We use detailed simulations to evaluate multiple configurations of detector networks and their ability to detect sources moving through a 6 × 6km ² area of downtown Chicago. A detector network’s probability of detecting a source increases with detector density but can also be increased with strategic node placement. We show that the ability to fuse correlated data from a source-carrying vehicle passing by multiple detectors can significantly contribute to the overall detection probability. In this paper we distinguish static sensor deployments operated as networks able to correlate signals between sensors, from deployments operated as arrays where each sensor is operated individually. In particular, we show that additional visual attributes of source-carrying vehicles, such as vehicle color and make, can greatly improve the ability of a detector network to detect illicit sources.