Feasibility of Remote Nuclear Reactor Antineutrino Directionality via Elastic Electron Scattering in the WATer CHerenkov Monitor of ANtineutrinos (WATCHMAN)

Cubic meter sized liquid scintillator detectors have demonstrated that the operational status, power level, and changes in fuel composition of a critical nuclear reactor system can be remotely measured with the antineutrino signal. With the success of these detectors, research has been pursued in the scaling of the detector size to increase sensitivity and standoff distance. One such detector is the WATer CHerenkov Monitor of ANtineutrinos (WATCHMAN). WATCHMAN is a kiloton-scale gadolinium-doped water Cherenkov detector, surrounded by approximately 4300 30.48 cm (12 inch) photomultiplier tubes (PMTs). The detector will utilize the inverse beta decay (IBD) interaction to measure the antineutrino rate and energy spectrum approximately 13 km away from a 3.758 GW(th) nuclear reactor. WATCHMAN will be the first to demonstrate the potential of gadolinium-doped water Cherenkov detectors for future nuclear reactor monitoring and safeguards applications. While IBD will enable WATCHMAN to measure the antineutrino rate and energy spectrum, the detector will not be sensitive enough to extract the direction of the incident antineutrinos from this process. Antineutrino directionality would be useful if multiple reactors are located near the detector, or if it is used to search for and locate clandestine reactors. This research investigated the potential of an alternative interaction, elastic antineutrino-electron scattering, to determine the direction of the incident antineutrino flux in WATCHMAN. Calculations were done to determine the expected scattering rate and Monte Carlo simulations were performed with GEANT4 to model detector response. Event reconstruction software was then used to reconstruct the directions of the scattered electrons based on the triggered PMT times, locations, and charge intensities. Estimated background rates were incorporated into the scattering signal by scaling reported measurements from similar detectors. Many potential sources of background were considered, including solar neutrinos and misidentified IBD interactions, gamma rays from the PMTs, detector walls, and surrounding rock, as well as the decays of cosmogenic radionuclides and water-borne radon. Preliminary results indicate that while most of the sources of background can be adequately controlled with strict detector component cleanliness and low radioactivity PMTs, radon levels consistent with other existing detectors are likely to exceed the acceptable limit for directional sensitivity in WATCHMAN

Free-moving Omnidirectional 3D Gamma-ray Imaging and Localization

The ability to localize and map the distribution of gamma-ray emitting radionuclides in 3D has applications in medical imaging, nuclear contamination remediation, and nuclear security and safeguards. The deployment of freely moving detection systems, such as hand-held instruments or ground/aerial-based vehicles, are critical in overcoming the inverse square law and complex shielding scenarios. Using auxiliary contextually-aware sensors, capable of perceiving spatiotemporal characteristics of the environment, these systems can simultaneously generate 3D maps of the surroundings and track the position and orientation of the gamma-ray sensitive detectors in the scene. The fusion of contextual scene data and gamma-ray detector data to facilitate real-time 3D gamma-ray image reconstruction has previously been demonstrated with mobile germanium and CdZnTe-based Compton cameras for gamma-ray energies ranging from a few hundred keV to several MeV. This concept is applied here for lower energy (50-400 keV) gamma-rays using an active coded mask imaging modality. The platform for demonstration is the Portable Radiation Imaging Spectroscopy and Mapping (PRISM) system, which is a hand-held spherical active coded array of many 1 cm3 coplanar-grid CdZnTe detectors designed for omnidirectional coded mask and Compton imaging and uniform directional sensitivity. This work presents the design, development, and coded mask optimization of PRISM, as well as the methodologies developed for real-time reconstruction using a scene data constrained, GPU-accelerated, list-mode maximum likelihood expectation maximization (ML-EM) algorithm. Experimental results from several measurements in the lab and in the field are shown.A novel approach to 3D gamma-ray image reconstruction for scenarios where sparsity in the source distribution may be assumed, for example radiological source search, is also presented. While the generality of ML-EM enables use in a wide variety of scenarios, it is susceptible to overfitting, limited by the discretization of spatial coordinates, and can be computationally expensive. A more well-conditioned Point-Source Localization (PSL) approach is formulated as an optimization problem where both position and source intensity are continuous variables. This formulation is then extended and generalized to an iterative algorithm for sparse parametric 3D image reconstruction called Additive Point-Source Localization (APSL), where the image is considered the sum of multiple point-sources whose position and intensity are continuous in nature. APSL mitigates overfitting in its iterative bottom-up nature and statistically-founded stopping criteria and, because of the inherent point-source assumption and continuous variables, results in images with improved accuracy and interpretability as compared with ML-EM. A set of simulated source search scenarios using a single non-directional detector is considered to demonstrate the concept and compare ML-EM and APSL. Experimental results using a nearly isotropic, contextually-aware, LaBr3 detector system are then presented, finding improved localization accuracy and computational efficiency with APSL

Free-moving Quantitative Gamma-ray Imaging

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, an algorithmic understanding 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.Comment: 19 pages, 5 figures, 4 supplementary figures, submitted to Scientific Reports - Natur

Free-moving Omnidirectional 3D Gamma-ray Imaging and Localization

The ability to localize and map the distribution of gamma-ray emitting radionuclides in 3D has applications in medical imaging, nuclear contamination remediation, and nuclear security and safeguards. The deployment of freely moving detection systems, such as hand-held instruments or ground/aerial-based vehicles, are critical in overcoming the inverse square law and complex shielding scenarios. Using auxiliary contextually-aware sensors, capable of perceiving spatiotemporal characteristics of the environment, these systems can simultaneously generate 3D maps of the surroundings and track the position and orientation of the gamma-ray sensitive detectors in the scene. The fusion of contextual scene data and gamma-ray detector data to facilitate real-time 3D gamma-ray image reconstruction has previously been demonstrated with mobile germanium and CdZnTe-based Compton cameras for gamma-ray energies ranging from a few hundred keV to several MeV. This concept is applied here for lower energy (50-400 keV) gamma-rays using an active coded mask imaging modality. The platform for demonstration is the Portable Radiation Imaging Spectroscopy and Mapping (PRISM) system, which is a hand-held spherical active coded array of many 1 cm3 coplanar-grid CdZnTe detectors designed for omnidirectional coded mask and Compton imaging and uniform directional sensitivity. This work presents the design, development, and coded mask optimization of PRISM, as well as the methodologies developed for real-time reconstruction using a scene data constrained, GPU-accelerated, list-mode maximum likelihood expectation maximization (ML-EM) algorithm. Experimental results from several measurements in the lab and in the field are shown.A novel approach to 3D gamma-ray image reconstruction for scenarios where sparsity in the source distribution may be assumed, for example radiological source search, is also presented. While the generality of ML-EM enables use in a wide variety of scenarios, it is susceptible to overfitting, limited by the discretization of spatial coordinates, and can be computationally expensive. A more well-conditioned Point-Source Localization (PSL) approach is formulated as an optimization problem where both position and source intensity are continuous variables. This formulation is then extended and generalized to an iterative algorithm for sparse parametric 3D image reconstruction called Additive Point-Source Localization (APSL), where the image is considered the sum of multiple point-sources whose position and intensity are continuous in nature. APSL mitigates overfitting in its iterative bottom-up nature and statistically-founded stopping criteria and, because of the inherent point-source assumption and continuous variables, results in images with improved accuracy and interpretability as compared with ML-EM. A set of simulated source search scenarios using a single non-directional detector is considered to demonstrate the concept and compare ML-EM and APSL. Experimental results using a nearly isotropic, contextually-aware, LaBr3 detector system are then presented, finding improved localization accuracy and computational efficiency with APSL

Neural Network Approaches for Mobile Spectroscopic Gamma-Ray Source Detection

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

Free-moving Quantitative Gamma-ray Imaging.

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