Proportional scintillation counters (PSCs), both single- and dual-phase, can measure the scintillation (S1) and ionization (S2) channels from particle interactions within the detector volume. The signal obtained from these detectors depends first on the physics of the medium (the initial scintillation and ionization), and second how the physics of the detector manipulates the resulting photons and liberated electrons. In this paper we develop a model of the detector physics that incorporates event topology, detector geometry, electric field configuration, purity, optical properties of components, and wavelength shifters. We present an analytic form of the model, which allows for general study of detector design and operation, and a Monte Carlo model which enables a more detailed exploration of S2 events. This model may be used to study systematic effects in current detectors such as energy and position reconstruction, pulse shape discrimination, event topology, dead time calculations, purity, and electric field uniformity. We present a comparison of this model with experimental data collected with an argon gas proportional scintillation counter (GPSC), operated at 20 C and 1 bar, and irradiated with an internal, collimated 55Fe source. Additionally we discuss how the model may be incorporated in Monte Carlo simulations of both GPSCs and dual-phase detectors, increasing the reliability of the simulation results and allowing for tests of the experimental data analysis algorithms.

This analysis uses source injection into background data collected by the Radiological Multi-sensor Analysis Platform (RadMAP) to characterize the performance of the Poisson Clutter Split algorithm and compare it with a region-of-interest algorithm. This comparison is performed for varying detector array sizes and false alarm rates using data from Sodium Iodide and High Purity Germanium detector arrays. The application of the Poisson Clutter Split algorithm is found to yield significant performance gains for both medium- and high-resolution detector arrays. Furthermore, trade-offs between energy resolution, array size, cost, and detection performance are explored. In doing so, it is shown that the choice of detection algorithm is a key factor in determining the overall system performance and should be an important consideration in system design.

Radiological source search is a challenging task involving detection and identification of weak sources in a constantly changing radiological background. As of now, many radiological source detection algorithms have been proposed; however, their computational complexity and, hence, reliance on power intensive processing units inhibit low-power applications of radiological source search systems. In this work, we introduce the anomaly filter (AF) algorithm; a computationally light, yet effective time-series source detection algorithm based on exponential weighted moving average (EWMA) and Poisson deviance statistics. Then, we demonstrate that the proposed algorithm can be used in ensemble with other more computationally intensive source detection and identification algorithms to achieve both increased detection performance and reduced power consumption. The proposed AF algorithm and the ensemble algorithms were thoroughly benchmarked against several existing source detection and identification algorithms. The results show that the AF algorithm outperforms existing conventional source detection algorithms, and the ensemble approach improves the overall performance of existing source detection and isotope identification algorithms. Furthermore, the AF algorithm and the non-negative matrix factorization approach-based source identification (NMF-ID) algorithm were combined and implemented on a single-board microcontroller, and the power consumption was measured. This ensemble algorithm reduced the power consumption of the NMF-ID algorithm almost by a factor of 100, while improving the detection performance of the overall system.

Radiological source search is a challenge involving the detection, identification, and localization of weak sources within background environments that vary both spatially and temporally. In this paper, a method for simultaneously detecting and identifying gamma-ray sources using background models formed from spectral data is described. Non-negative matrix factorization (NMF) is used to generate low-dimensional representations of gamma-ray spectra, allowing for a compact means of capturing variation in gamma-ray backgrounds. Background models formed using NMF are used to perform anomaly detection, and additionally, models are augmented with spectral templates of gamma-ray sources to perform simultaneous detection and identification using a likelihood ratio test. The NMF-based detection and identification algorithm is benchmarked against a standard Region of Interest algorithm and shows significant performance gains. In addition, NMF-based anomaly detection shows improvements over methods based on gross counts or principal component analysis. Algorithm performance is evaluated using unshielded sources with activities between 5 and 400 {\mu } Ci at a standoff distance of 20 m using source injection on background data collected using a 1 m2 NaI array on the Radiological Multisensor Analysis Platform.

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.