In gamma spectrometers with variable spectroscopic performance across many channels (e.g., many pixels or voxels), a tradeoff exists between including data from successively worse-performing readout channels and increasing efficiency. Brute-force calculation of the optimal set of included channels is exponentially infeasible as the number of channels grows, and approximate methods are required. In this work, we present a data-driven framework for attempting to find near-optimal sets of included detector channels. The framework leverages non-negative matrix factorization (NMF) to learn the behavior of gamma spectra across the detector, and clusters similarly-performing detector channels together. Performance comparisons are then made between spectra with channel clusters removed, which is more feasible than brute force. The framework is general and can be applied to arbitrary, user-defined performance metrics depending on the application. We apply this framework to optimizing gamma spectra measured by H3D M400 CdZnTe spectrometers, which exhibit variable performance across their crystal volumes. In particular, we show several examples optimizing various performance metrics for uranium and plutonium gamma spectra in nondestructive assay for nuclear safeguards, and explore trends in performance vs. parameters such as clustering algorithm type. We also compare the NMF+clustering pipeline to several non-machine-learning algorithms, including several greedy algorithms. Overall, we find that the NMF+clustering pipeline tends to find the best-performing set of detector voxels, significantly improving over the un-optimized spectra, but that a greedy accumulation of spectra segmented by detector depth can in some cases give similar performance improvements in much less computation time.

In this third part of a multi-paper series, we present quantitative image reconstruction results from aerial measurements of eight different surrogate distributed gamma-ray sources on flat terrain. We show that our quantitative imaging methods can accurately reconstruct the expected shapes, and, after appropriate calibration, the absolute activity of the distributed sources. We conduct several studies of imaging performance versus various measurement and reconstruction parameters, including detector altitude and raster pass spacing, data and modeling fidelity, and regularization type and strength. The imaging quality performance is quantified using various quantitative image quality metrics. Our results confirm the utility of point source arrays as surrogates for truly distributed radiological sources, and advance the quantitative capabilities of Scene Data Fusion gamma-ray imaging methods.

We have developed a CLLBC-based gamma- and neutron-sensitive multi-channel omnidirectional imaging detector, suitable for handheld or vehicle-borne operation and capable of quantitative radiation mapping in 3D. The system comprises 62 CLLBC modules in an active-masked configuration, and is coupled to a Localization and Mapping Platform (LAMP) suite of contextual sensors that provides a 3D map of the environment. The contextual and radiation data is combined using Scene Data Fusion (SDF) methods to better inform the reconstruction of the source radiation distribution from variations in the measured counts as the detector moves throughout the 3D environment. Here, we first present benchtop-scale characterization studies for both the neutron and gamma ray channels. In tandem, we present Geant4 simulations of both the single-crystal and full-system detection efficiencies over the omnidirectional field of view, and compare against validation measurements. We then demonstrate the imager's capabilities in a variety of different scenarios, ranging from free-moving handheld simultaneous measurements of Cs-137 and Cf-252 to more challenging motion-constrained or static measurement scenarios. In several of these scenarios we also demonstrate how the full omnidirectional multi-crystal responses markedly improve the reconstruction quality. The imager is therefore a promising system for conducting simultaneous gamma and neutron radiation measurements in applications such as homeland security, contamination mapping, and nuclear decommissioning.

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.

In this second part of a multi-paper series, we present results from outdoor aerial measurements of surrogate distributed gamma-ray sources. We detail the design, manufacture, and testing of 300 individual ~7 mCi Cu-64 sealed sources at the Washington State University research reactor, and their deployment in various source patterns (each comprising up to 100 point sources) during the aerial measurement campaign. We show the results of two such measurements, in which approximate source shapes and qualitative source intensities can be seen from the count rate vs. position plots, even without performing reconstructions. We also detail our efforts in ground-truthing the deployed sources and comparing measured gamma ray data to model predictions. In particular we compare measured vs. expected count data using the Poisson deviance formalism of Part I to evaluate whether the fielded surrogate point-source arrays “look like” their truly continuous distributed source analogues. More generally, we find that the point-source array technique provides high source placement accuracy, relative ease of quantifying the true source configuration, scalability to source dimensions of ≲100 m, ease of reconfiguration and removal, and relatively low dose to personnel. Finally, we consider potential improvements and generalizations of the point-source array technique for future measurement campaigns.

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.

Machine learning has been found to be ubiquitously useful across many industries, presenting an opportunity to improve radiation detection performance using data-driven algorithms. Improved detector resolution can aid in the detection, identification, and quantification of radionuclides. In this work, a novel, data-driven, unsupervised learning approach is developed to improve detector spectral characteristics by learning, and subsequently rejecting, poorly performing regions of the pixelated detector. Feature engineering is used to fit individual characteristic photo peaks to a Doniach lineshape with a linear background model. Then, principal component analysis is used to learn a lower-dimension latent space representation of each photo peak where the pixels are clustered, and subsequently ranked, based on the cluster mean distance to an optimal point. Pixels within the worst cluster(s) are rejected to improve the full-width at half-maximum (FWHM) by 10% to 15% (relative to the bulk detector) at 50% net efficiency when applied to training data obtained from measurements of a 100 mu Ci 154Eu source using a H3D M400i pixelated cadmium zinc telluride detector.These results compare well with, but do not outperform, a greedy algorithm that accumulates pixels in order of FWHM from lowest to highest used as a benchmark. In the future, this approach can be extended to include the detector energy and angular response. Finally, the model is applied to newly seen natural and enriched uranium spectra relevant for nuclear safeguards applications.

In this first part of a multipaper series, we demonstrate a method for using arrays of point sources to emulate continuously distributed gamma-ray sources when measured from a standoff of at least several meters. The method relies on the Poisson deviance statistic to test whether the array source "looks like" its continuous analog when measured by a particular gamma-ray detector moving through 3-D space on a particular trajectory. This point-source method offers significant advantages over truly distributed sources such as powders, solutions, or aerosols; notably, arrays of sealed point sources are safer to both personnel and the environment, and are more easily deployed, reconfigured, ground-truthed, and removed. We use this Poisson deviance metric to design eight different mock distributed sources, ranging in complexity from a 3 x 36 m uniform square grid of 5 mCi Cu-64 sources to a configuration where regions of higher and zero activity are superimposed on a uniform baseline. We then present several example calculations for various detector systems, altitudes, array source spacings, and source patterns, and examine under what parameters it is possible to design a surrogate array source that is nearly indistinguishable from a truly continuous distributed source. In Part II, we will detail the design, manufacture, and testing of Cu-64 sealed sources at the Washington State University research reactor, discuss their deployment during the aerial measurement campaign, and present results from several measurements.

The detection, identification, and localization of illicit radiological and nuclear material continue to be key components of nuclear non-proliferation and nuclear security efforts around the world. Networks of radiation detectors deployed at strategic locations in urban environments have the potential to provide continuous radiological/nuclear (R/N) surveillance and provide high probabilities of intercepting threat sources. The integration of contextual information from sensors such as video, Lidar, and meteorological sensors can provide significantly enhanced situational awareness, and improved detection and localization performance through the fusion of the radiological and contextual data. In this work, we present details of our work to establish a city-scale multi-sensor network testbed for intelligent, adaptive R/N detection in urban environments, and develop new techniques that enable city-scale source detection, localization, and tracking.

The addition of contextual sensors to mobile radiation sensors provides valuable information about radiological source encounters that can assist in adjudication of alarms. This study explores how computer-vision based object detection and tracking analyses can be used to augment radiological data from a mobile detector system. We study how contextual information (streaming video and LiDAR) can be used to associate dynamic pedestrians or vehicles with radiological alarms to enhance both situational awareness and detection sensitivity. Possible source encounters were staged in a mock urban environment where participants included pedestrians and vehicles moving in the vicinity of an intersection. Data was collected with a vehicle equipped with 6 NaI(Tl) 2 inch times 4 inch times 16 inch detectors in a hexagonal arrangement and multiple cameras, LiDARs, and an IMU. Physics-based models that describe the expected count rates from tracked objects are used to correlate vehicle and/or pedestrian trajectories to measured count-rate data through the use of Poisson maximum likelihood estimation and to discern between source-carrying and non-source-carrying objects. In this work, we demonstrate the capabilities of our source-object attribution approach as applied to a mobile detection system in the presence of moving sources to improve both detection sensitivity and situational awareness in a mock urban environment.

The ability to formulate maps of minimum detectable activities (MDAs) that describe the sensitivity of an ad hoc measurement that used one or more freely moving radiation detector systems would be significantly beneficial for the conduct and understanding of many radiological search activities. In a real-time scenario with a free-moving detector system, an MDA map can provide useful feedback to the operator about which areas have not been searched as thoroughly as others, thereby allowing the operator to prioritize future actions. Similarly, such a calculation could be used to inform subsequent navigation decisions of autonomous platforms. Here we describe a near real-time MDA mapping approach that can be applied when searching for point sources using detected events in a spectral region of interest (ROI) while assuming a constant, unknown background rate. We show the application of this MDA mapping method to a real scenario, a survey of the interior of a small building using a handheld detector system. Repeated measurements with no sources and with 137Cs sources of different strengths yield results consistent with the estimated thresholds and MDA values; namely, that for background-only measurements no sources are seen above threshold anywhere in the scene, while when sources are present they are detected above the thresholds calculated for their locations.

Static gamma-ray detector systems that are deployed outdoors for radiological monitoring purposes experience time-varying 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 (KL) 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.

Contextual sensors can be equipped to radiation detection systems to allow free-moving 3D gamma-ray imaging through scene data fusion. Scene data fusion captures relevant scene information to construct a 3D map of an environment, which can be used to constrain image reconstruction techniques. While developed to enable free-moving imaging measurements, scene data fusion also has applications in cases that require multiple static measurements to generate 3D images, where the scene information can improve results. One way we seek to further leverage contextual scene information is to use the 3D map to automatically identify the radiological object of interest in a scene, and then determine optimal measurement poses around that object for quantitative Compton imaging. We also seek to improve the use of contextual information by identifying materials present in an environment to inform attenuation estimates for intervening materials. We present the approach for further leveraging the contextual information, and the preliminary results of using an SDF-enabled gamma-ray imager to find optimal measurement positions for a distributed source scenario to produce quantitative Compton images with minimization of associated uncertainties.

By combining radiation detection technologies with robotics sensing, the ability to continuously conduct gamma-ray imaging using freely-moving systems was demonstrated in 2015.(1) This new method, which was named free-moving 3D Scene Data Fusion (SDF), was then applied to mapping radioactive contamination and to contextualizing the extent of contamination and the efficacy of radiological clean-up efforts.(2, 3) Since then, further studies into the types of radiation detection systems to which SDF could be applied resulted in the discovery and demonstration that neutron activity could be mapped using neutron-sensitive CLLBC scintillators, arrays of pixelated CZT detectors could be used to create multi-modal imagers, and more rudimentary detector systems such as arrays of four CsI modules could still achieve good-quality mapping by inferring source positioning through the encoded modulation of source-to-detector distance. This paper provides an overview of the SDF technology, highlights recent measurements leveraging SDF-equipped systems, discusses the continued development of quantitative algorithms(4, 5) and their ramifications for developing autonomous SDF-capabilities, and summarizes future directions of research and application development for free moving radiation detection systems.

We demonstrate a method for using arrays of point sources that emulate -- when measured from a standoff of at least several meters -- distributed gamma-ray sources, and present results using this method from outdoor aerial measurements of several planar arrays each comprising up to $100$ ${\sim}7$ mCi Cu-64 sealed sources. The method relies on the Poisson deviance to statistically test whether the array source ''looks like'' its continuous analogue to a particular gamma-ray detector given the counts recorded as the detector moves about 3D space. We use this deviance metric to design eight different mock distributed sources, ranging in complexity from a $36\times36$ m uniform square grid of sources to a configuration where regions of higher and zero activity are superimposed on a uniform baseline. We then detail the design, manufacture, and testing of the ${\sim} 7$ mCi Cu-64 sealed sources at the Washington State University research reactor, and their deployment during the aerial measurement campaign. We show the results of two such measurements, in which approximate source shapes and qualitative source intensities can be seen. Operationally, we find that the point-source array technique provides high source placement accuracy and ease of quantifying the true source configuration, scalability to source dimensions of ${\lesssim}100$ m, ease of reconfiguration and removal, and relatively low dose to personnel. Finally, we consider potential improvements and generalizations of the point-source array technique for future measurement campaigns.

Using a series of detector measurements taken at different locations to localize a source of radiation is a well-studied problem. The source of radiation is sometimes constrained to a single point-like source, in which case the location of the point source can be found using techniques such as maximum likelihood. Recent advancements have shown the ability to locate point sources in 2-D and even 3-D but few have studied the effect of intervening material on the problem. In this work, we examine gamma-ray data taken from a freely moving system and develop voxelized 3-D models of the scene using data from its onboard light detection and ranging (LiDAR) unit. Ray casting is used to compute the distance each gamma ray travels through the scene material, which is then used to calculate attenuation assuming a single attenuation coefficient for solids within the geometry. Parameter estimation using maximum likelihood is performed to simultaneously find the attenuation coefficient, source activity, and source position that best match the data. Using a simulation, we validate the ability of this method to reconstruct the true location and activity of a source, along with the true attenuation coefficient of the structure it is inside, and then we apply the method to measured data with sources and find good agreement.

Artificial neural networks (ANNs) for performing spectroscopic gamma-ray source identification have been previously introduced, primarily for applications in controlled laboratory settings. To understand the utility of these methods in scenarios and environments more relevant to nuclear safety and security, this work examines the use of ANNs for mobile detection, which involves highly variable gamma-ray background, low signal-to-noise ratio measurements, and low false alarm rates. Simulated data from a 2” × 4” × 16” NaI(Tl) detector are used in this work for demonstrating these concepts, and the minimum detectable activity (MDA) is used as a performance metric in assessing model performance.In addition to examining simultaneous detection and identification, binary spectral anomaly detection using autoencoders is introduced in this work, and benchmarked using detection methods based on Non-negative Matrix Factorization (NMF) and Principal Component Analysis (PCA). On average, the autoencoder provides a 12% and 23% improvement over NMF- and PCA-based detection methods, respectively. Additionally, source identification using ANNs is extended to leverage temporal dynamics by means of recurrent neural networks, and these time-dependent models outperform their time-independent counterparts by 17% for the analysis examined here. The paper concludes with a discussion on tradeoffs between the ANN-based approaches and the benchmark methods examined here.

Networked detector systems can be deployed in urban environments to aid in the detection and localization of radiological and/or nuclear material. However, effectively responding to and interpreting a radiological alarm using spectroscopic data alone may be hampered by a lack of situational awareness, particularly in complex environments. This study investigates the use of Light Detection and Ranging (LiDAR) and streaming video to enable real-time object detection and tracking, and the fusion of this tracking information with radiological data for the purposes of enhanced situational awareness and increased detection sensitivity. This work presents an object detection, tracking, and novel source-object attribution analysis that is capable of operating in real time. By implementing this analysis pipeline on a custom-developed system that comprises a static 2 in. × 4 in. × 16 in. NaI(Tl) detector colocated with a 64beam LiDAR and four monocular cameras, we demonstrate the ability to accurately correlate trajectories from tracked objects to spectroscopic gamma-ray data in real time and use physics-based models to reliably discriminate between source-carrying and nonsource-carrying objects. In this work, we describe our approach in detail and present a quantitative performance assessment that characterizes the source-object attribution capabilities of both video and LiDAR. Additionally, we demonstrate the ability to simultaneously track pedestrians and vehicles in a mock urban environment and use this tracking information to improve both detection sensitivity and situational awareness using our contextual-radiological data fusion methodology.

The ability to map and estimate the activity of radiological source distributions in unknown three-dimensional environments has applications in the prevention and response to radiological accidents or threats as well as the enforcement and verification of international nuclear non-proliferation agreements. Such a capability requires well-characterized detector response functions, accurate time-dependent detector position and orientation data, a digitized representation of the surrounding 3D environment, and appropriate image reconstruction and uncertainty quantification methods. We have previously demonstrated 3D mapping of gamma-ray emitters with free-moving detector systems on a relative intensity scale using a technique called Scene Data Fusion (SDF). Here we characterize the detector response of a multi-element gamma-ray imaging system using experimentally benchmarked Monte Carlo simulations and perform 3D mapping on an absolute intensity scale. We present experimental reconstruction results from hand-carried and airborne measurements with point-like and distributed sources in known configurations, demonstrating quantitative SDF in complex 3D environments.