32 research outputs found
Reconstructing the direction of reactor antineutrinos via electron scattering in Gd-doped water Cherenkov detectors
The potential of elastic antineutrino-electron scattering in a Gd-doped water
Cherenkov detector to determine the direction of a nuclear reactor antineutrino
flux was investigated using the recently proposed WATCHMAN antineutrino
experiment as a baseline model. The expected scattering rate was determined
assuming a 13-km standoff from a 3.758-GWt light water nuclear reactor and the
detector response was modeled using a Geant4-based simulation package.
Background was estimated via independent simulations and by scaling published
measurements from similar detectors. Background contributions were estimated
for solar neutrinos, misidentified reactor-based inverse beta decay
interactions, cosmogenic radionuclides, water-borne radon, and gamma rays from
the photomultiplier tubes (PMTs), detector walls, and surrounding rock. We show
that with the use of low background PMTs and sufficient fiducialization,
water-borne radon and cosmogenic radionuclides pose the largest threats to
sensitivity. Directional sensitivity was then analyzed as a function of radon
contamination, detector depth, and detector size. The results provide a list of
experimental conditions that, if satisfied in practice, would enable
antineutrino directional reconstruction at 3 significance in large
Gd-doped water Cherenkov detectors with greater than 10-km standoff from a
nuclear reactor.Comment: 11 pages, 9 figure
Mobile Object Tracking in Panoramic Video and LiDAR for Radiological Source-Object Attribution and Improved Source Detection
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
Background and Anomaly Learning Methods for Static Gamma-ray Detectors
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.Comment: 12 pages, 6 figures, accepted for publication in IEEE Transactions on
Nuclear Scienc
The EU-ToxRisk method documentation, data processing and chemical testing pipeline for the regulatory use of new approach methods
Hazard assessment, based on new approach methods (NAM), requires the use of batteries of assays, where individual tests may be contributed by different laboratories. A unified strategy for such collaborative testing is presented. It details all procedures required to allow test information to be usable for integrated hazard assessment, strategic project decisions and/or for regulatory purposes. The EU-ToxRisk project developed a strategy to provide regulatorily valid data, and exemplified this using a panel of > 20 assays (with > 50 individual endpoints), each exposed to 19 well-known test compounds (e.g. rotenone, colchicine, mercury, paracetamol, rifampicine, paraquat, taxol). Examples of strategy implementation are provided for all aspects required to ensure data validity: (i) documentation of test methods in a publicly accessible database; (ii) deposition of standard operating procedures (SOP) at the European Union DB-ALM repository; (iii) test readiness scoring accoding to defined criteria; (iv) disclosure of the pipeline for data processing; (v) link of uncertainty measures and metadata to the data; (vi) definition of test chemicals, their handling and their behavior in test media; (vii) specification of the test purpose and overall evaluation plans. Moreover, data generation was exemplified by providing results from 25 reporter assays. A complete evaluation of the entire test battery will be described elsewhere. A major learning from the retrospective analysis of this large testing project was the need for thorough definitions of the above strategy aspects, ideally in form of a study pre-registration, to allow adequate interpretation of the data and to ensure overall scientific/toxicological validity.Toxicolog
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Unsupervised Learning for Improved Gamma-Ray Spectrometry in Pixelated Cadmium Zinc Telluride (CZT) Detectors
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 μCi 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
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A Spherical Active Coded Aperture for Gamma-Ray Imaging
Gamma-ray imaging facilitates the efficient detection, characterization, and localization of compact radioactive sources in cluttered environments. Fieldable detector systems employing active planar coded apertures have demonstrated broad energy sensitivity via both coded aperture and Compton imaging modalities. However, planar configurations suffer from a limited field of view, especially in the coded aperture mode. To improve upon this limitation, we introduce a novel design by rearranging the detectors into an active coded spherical configuration, resulting in a 4Ï€ isotropic field of view for both coded aperture and Compton imaging. This paper focuses on the low-energy coded aperture modality and the optimization techniques used to determine the optimal number and configuration of 1-cm3 CdZnTe coplanar grid detectors on a 14-cm diameter sphere with 192 available detector locations