As more complex predictive models are used for gamma-ray spectral analysis, methods are needed to probe and understand their predictions and behavior. Recent work has begun to bring the latest ...techniques from the field of Explainable Artificial Intelligence (XAI) into the applications of gamma-ray spectroscopy, including the introduction of gradient-based methods like saliency mapping and Gradient-weighted Class Activation Mapping (Grad-CAM), and black box methods like Local Interpretable Model-agnostic Explanations (LIME) and SHapley Additive exPlanations (SHAP). In addition, new sources of synthetic radiological data are becoming available, and these new data sets present opportunities to train models using more data than ever before. In this work, we use a neural network model trained on synthetic NaI(Tl) urban search data to compare some of these explanation methods and identify modifications that need to be applied to adapt the methods to gamma-ray spectral data. We find that the black box methods LIME and SHAP are especially accurate in their results, and recommend SHAP since it requires little hyperparameter tuning. We also propose and demonstrate a technique for generating counterfactual explanations using orthogonal projections of LIME and SHAP explanations.
The enormous advances in sensing and data processing technologies in combination with recent developments in nuclear radiation detection and imaging enable unprecedented and "smarter" ways to detect, ...map, and visualize nuclear radiation. The recently developed concept of three-dimensional (3-D) Scene-data fusion allows us now to "see" nuclear radiation in three dimensions, in real time, and specific to radionuclides. It is based on a multi-sensor instrument that is able to map a local scene and to fuse the scene data with nuclear radiation data in 3-D while the instrument is freely moving through the scene. This new concept is agnostic of the deployment platform and the specific radiation detection or imaging modality. We have demonstrated this 3-D Scene-data fusion concept in a range of configurations in locations, such as the Fukushima Prefecture in Japan or Chernobyl in Ukraine on unmanned and manned aerial and ground-based platforms. It provides new means in the detection, mapping, and visualization of radiological and nuclear materials relevant for the safe and secure operation of nuclear and radiological facilities or in the response to accidental or intentional releases of radioactive materials where a timely, accurate, and effective assessment is critical. In addition, the ability to visualize nuclear radiation in 3-D and in real time provides new means in the communication with public and facilitates to overcome one of the major public concerns of not being able to "see" nuclear radiation.
Abstract
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.
We present Data Releases 4 and 5 of the quasar catalog from the quasar survey by the Large Sky Area Multi-object Fiber Spectroscopic Telescope (LAMOST), which includes quasars observed between 2015 ...September and 2017 June. There are a total of 19,253 quasars identified by visual inspections of the spectra. Among them, 11,458 were independently discovered by LAMOST, in which 3296 were reported by the SDSS DR12 and DR14 quasar catalog after our survey began, while the remaining 8162 are new discoveries of LAMOST. We provide the emission line measurements for H , Hβ, Mg ii, and/or C iv for 18,100 quasars. Since LAMOST does not have absolute flux calibration information, we obtain the monochromatic continuum luminosities by fitting the SDSS photometric data using the quasar spectra, and then estimate the black hole masses. The catalog and spectra for these quasars are available online. This is the third installment in the series of LAMOST quasar surveys that has released spectra for ∼43,000 quasars to date. There are 24,772 independently discovered quasars, 17,128 of which are newly discovered. In addition to this great supplement to the new quasar discoveries, LAMOST has also provided a large database (overlapped with SDSS) for investigating quasar spectral variability and discovering unusual quasars, including changing-look quasars, with ongoing and upcoming large surveys.
We present the first measurements of the angular dependence of the betatron x-ray spectrum produced by electrons inside the cavity of a laser-wakefield accelerator. Electrons accelerated up to ...300 MeV energies produce a beam of broadband, forward-directed betatron x-ray radiation extending up to 80 keV. The angular resolved spectrum from an image plate-based spectrometer with differential filtering provides data in a single laser shot. The simultaneous spectral and spatial x-ray analysis allows for a three-dimensional reconstruction of electron trajectories with micrometer resolution, and we find that the angular dependence of the x-ray spectrum is showing strong evidence of anisotropic electron trajectories.
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.
When searching for radiological sources in an urban area, a vehicle-borne detector system will often measure complex, varying backgrounds primarily from natural gamma-ray sources. Much work has been ...focused on developing spectral algorithms that retain sensitivity and minimize the false-positive rate even in the presence of such spectral and temporal variability. However, information about the environment surrounding the detector system might also provide useful clues about the expected background, which if incorporated into an algorithm, could improve performance. Recent work has focused on extensive measuring and modeling of urban areas with the goal of understanding how these complex backgrounds arise. This work presents an analysis of panoramic video images and gamma-ray background data collected in Oakland, California, by the radiological multisensor analysis platform (RadMAP) vehicle. Features were extracted from the panoramic images by semantically labeling the images and then convolving the labeled regions with the detector response. A linear model was used to relate the image-derived features to gamma-ray spectral features obtained using nonnegative matrix factorization (NMF) under different regularizations. We find some gamma-ray background features correlate strongly with image-derived features that measure the response-adjusted solid angle subtended by sky and buildings, and we discuss the implications for the development of future, contextually aware detection algorithms.
Airborne gamma-ray surveys are useful for many applications, ranging from geology and mining to public health and nuclear security. In all these contexts, the ability to decompose a measured spectrum ...into a linear combination of background source terms can provide useful insights into the data and lead to improvements in the techniques that use spectral energy windows. Multiple methods for the linear decomposition of spectra exist but are subject to various drawbacks, such as allowing negative photon fluxes or requiring detailed Monte Carlo modeling. We propose using non-negative matrix factorization (NMF) as a data-driven approach to spectral decomposition. Using aerial surveys that include flights over water, we demonstrate that the mathematical approach of NMF finds physically relevant structure in the aerial gamma-ray background, namely, that measured spectra can be expressed as the sum of nearby terrestrial emission, distant terrestrial emission, and radon and cosmic emission. These NMF background components are compared with the background components obtained by noise-adjusted singular value decomposition (NASVD), which contain negative photon fluxes and, thus, do not represent the emission spectra in as straightforward a way. Finally, we comment on the potential areas of research that are enabled by NMF decompositions, such as new approaches to spectral anomaly detection and data fusion.
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 <inline-formula> <tex-math notation="LaTeX">36\,\, {}\times {}36 </tex-math></inline-formula> 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.
Gamma-ray imaging attempts to reconstruct the spatial and intensity distribution of gamma-emitting radionuclides from a set of measurements. Generally, this problem is solved by discretizing the ...spatial dimensions and employing the maximum likelihood expectation maximization (ML-EM) algorithm, with or without some form of regularization. While the generality of this formulation 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. We present a novel approach to 3D gamma-ray image reconstruction for scenarios where sparsity may be assumed, for example, radiological source search. In this paper, we first formulate a point-source localization (PSL) approach as an optimization problem, where both position and source intensity are continuous variables. We then extend and generalize this formulation to an iterative algorithm, called additive PSL (APSL), for sparse parametric image reconstruction. A set of simulated source search scenarios using a single non-directional detector are considered, finding improved image accuracy and computational efficiency with APSL over traditional grid-based approaches.
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