Radiation detection can provide a reliable means of detecting radiological material. Such capabilities can help to prevent nuclear and/or radiological attacks, but reliable detection in uncontrolled surroundings requires algorithms that account for environmental background radiation. The Berkeley Data Cloud (BDC) facilitates the development of such methods by providing a framework to capture, store, analyze, and share data sets. In the era of big data, both the size and variety of data make it difficult to explore and find data sets of interest and manage the data. Thus, in the context of big data, visualization is critical for checking data consistency and validity, identifying gaps in data coverage, searching for data relevant to an analyst's use cases, and choosing input parameters for analysis. Downloading the data and exploring it on an analyst's desktop using traditional tools are no longer feasible due to the size of the data. This paper describes the design and implementation of a visualization system that addresses the problems associated with data exploration within the context of the BDC. The visualization system is based on a JavaScript front end communicating via REST with a back end web server.

The variability of gamma-ray and neutron background during the operation of a mobile detector system greatly limits the ability of the system to detect weak radiological and nuclear threats. The natural radiation background measured by a mobile detector system is the result of many factors, including the radioactivity of nearby materials, the geometric configuration of those materials and the system, the presence of absorbing materials, and atmospheric conditions. Background variations tend to be highly non-Poissonian, making it difficult to set robust detection thresholds using knowledge of the mean background rate alone. The Radiological Multi-sensor Analysis Platform (RadMAP) system is designed to allow the systematic study of natural radiological background variations and to serve as a development platform for emerging concepts in mobile radiation detection and imaging. To do this, RadMAP has been used to acquire extensive, systematic background measurements and correlated contextual data that can be used to test algorithms and detector modalities at low false alarm rates. By combining gamma-ray and neutron detector systems with data from contextual sensors, the system enables the fusion of data from multiple sensors into novel data products. The data are curated in a common format that allows for rapid querying across all sensors, creating detailed multi-sensor datasets that are used to study correlations between radiological and contextual data, and develop and test novel techniques in mobile detection and imaging. In this paper we will describe the instruments that comprise the RadMAP system, the effort to curate and provide access to multi-sensor data, and some initial results on the fusion of contextual and radiological data.

The presence of gamma-ray background significantly reduces detection sensitivity when searching for radioactive sources in the field, and the systematic variability in the background will limit the size and energy resolution of systems that can be used effectively. An extensive survey of the background was performed using both sodium iodide and high-purity germanium. By using a bivariate negative binomial model for the measured counts, these measurements can be resampled to simulate the performance of a detector array of arbitrary size and resolution. The response of the system as it moved past a stationary source was modeled for spectroscopic and coded aperture imaging algorithms and used for source injection into the background. The performance of both techniques is shown for various sizes and resolutions, as well as the relative performance for sodium iodide and germanium. It was found that at smaller detector sizes or better energy resolution, spectroscopy has higher detection sensitivity than imaging, while imaging is better suited to larger or poorer resolution detectors.

The presence of gamma-ray background significantly reduces detection sensitivity when searching for radioactive sources in the field, particularly in mobile systems which must contend with a variable background that is not known a priori . An extensive survey of the background was performed in the San Francisco Bay Area using both sodium iodide and high-purity germanium detectors, covering a wide variety of environments that might be encountered in an operational scenario. This data was used as a basis for source injection in a moving detector scenario in order to assess the effects of the background on different detection approaches. Both imaging and spectroscopic algorithms were implemented for the sodium iodide array, and their performances are compared for a variety of source energies and stand-off distances in the presence of the measured background.

The Gamma-Ray Imaging Framework (GRIF) is an open source (LGPL) software framework for creating real-time gamma-ray imaging applications. GRIF is written in C++ using Qt, and uses ROOT and the Boost Graph Library. GRIF provides automatic multi-threading and data management to make it easy to quickly develop power gamma-ray imagining applications. The model for application developers is built around the separation of data acquisition (DAQ) and analysis units. Users are expected to use the APIs for the DAQ and analysis units to build their applications. Memory and data are managed by GRIF, so the user does not need to worry about allocating and de-allocating memory for data or thread management and locking schemes. The user only needs to post data to and read data from the GRIF memory manager. GRIF uses XML configuration files for determining data dependences between DAQ and analysis units in the system. We will give an overview of the first release of GRIF, as well as show example applications that have been built using the framework.

In gamma-ray spectroscopy and imaging, the natural gamma-ray background can significantly reduce detection sensitivity, especially when the source is weak and the background varies substantially. This project aims to systematically measure and characterize the spatial and temporal variations of the background in order to assess their impact on detection sensitivity and specificity for homeland security applications. An extensive survey of typical backgrounds found in the San Francisco bay area was performed, and initial measurement results are presented here.

In general, the reconstructed image in coded aperture imaging is affected by the source configuration. Fenimore's balanced convolution method in conjunction with the uniformly redundant array can remove the interference due to the source configuration. As an extension of Fenimore's balanced convolution method, we present general conditions for designing an unbiased mean estimator for a far-field coded aperture imaging system with a random binary mask. As part of the general conditions, we propose decoding arrays whose elements are variable with respect to source directions. We also show that the unbiased mean estimator from Fenimore's balanced convolution method is a special case of the general conditions. We also present a practical example of designing restoring arrays for a coded aperture system with a random mask.

Electron-tracking-based Compton imaging of gamma rays reduces the background level of the backprojected Compton image through the additional measurement of the initial momentum vector of the Compton electron. This reduction in image background has the potential for the detection of weaker sources in a complex background radiation field. Electron-tracking-based Compton imaging was demonstrated recently in solid-state detectors through the use of scientific Si charge-coupled devices (CCDs) with excellent position and energy resolution characteristics. In addition, the sensitivity of the electron track reconstruction algorithm has been evaluated extensively on the modeled detector response to Monte-Carlo electron tracks. We have now benchmarked the modeled algorithm sensitivity with our experimentally observed algorithm sensitivity, by measuring CCD electron tracks from a collimated 662 keV gamma-ray source in coincidence with a position-sensitive HPGe detector. For all coincident events the electron momentum vector deduced by the reconstruction algorithm is compared to the electron momentum vector calculated from the measured positions. This measured distribution of angular error of the algorithm agrees well with the angular error distribution calculated from our electron transport and detector models.

We present a quantitative method for relating system matrices to the complete-data space in maximum likelihood expectation maximization (MLEM) using the Kullback-Leibler distance. We show that a more accurate system matrix has a smaller Kullback-Leibler (KL) distance. System matrices of a coded aperture imaging system were used for comparison. The calculation of the KL distance is based on the Monte Carlo integral. We note that system matrices for the KL distance evaluation should be generated by underlying physics processes.

An analytical model for the generation of β-delayed γ-ray spectra following thermal-neutron-induced fission of mixed samples of 235U and 239Pu is presented. Using an energy-dependent figure-of-merit to designate the spectral regions employed in the assay, the unique temporal β-delayed γ-ray signatures are utilized to determine the fraction of 239Pu in a mixed U–Pu sample. By evaluating the β-delayed γ-ray temporal signatures of both 235U and 239Pu within a 3keV energy bin, traditional sources of systematic uncertainty in quantitative assay using β-delayed γ-ray signals, such as self-attenuation of the sample and energy-dependent γ-ray detection efficiency, are significantly reduced. The effects of the time-dependent Compton-continuum and growth of longer-lived nuclides on the quantitative assessment are explored. This methodology represents a promising extension of the conventional means of analysis for quantitative assay of fissile materials using β-delayed γ-ray signatures.

We have implemented benchmarked models to determine the gain in sensitivity of electron-tracking based Compton imaging relative to conventional Compton imaging by the use of high-resolution scientific charge-coupled devices (CCD). These models are based on the recently demonstrated ability of electron-tracking based Compton imaging by using fully depleted scientific CCDs. Here we evaluate the gain in sensitivity by employing Monte Carlo simulations in combination with advanced charge transport models to calculate two-dimensional charge distributions corresponding to experimentally obtained tracks. In order to reconstruct the angle of the incident γ-ray, a trajectory determination algorithm was used on each track and integrated into a back-projection routine utilizing a geodesic-vertex ray tracing technique. Analysis was performed for incident γ-ray energies of 662keV and results show an increase in sensitivity consistent with tracking of the Compton electron to approximately ±30°.

Compton imaging has been demonstrated to provide excellent detection and localization capabilities in the search and characterization of radiation sources. However, the currently achievable sensitivity is limited by the Compton cone, which is backprojected. By measuring the initial trajectory of the Compton electron, the cone may be reduced to a cone segment with a corresponding increase in sensitivity.We have demonstrated the ability to measure electron trajectories (tracks) in thick (650 mu m), fully depleted silicon scientific CCDs, with a spatial resolution of 10 mu m in 2D. These measured tracks have been used to benchmark simulations of electron physics and detector response. We have developed an electron track algorithm to measure the initial electron direction in 3D from the CCD image, and utilized the modeled electron tracks to evaluate the angular resolution as a function of energy and initial direction for electrons up to similar to 500 key. For electrons above 150 keV and 30 degrees out-of-plane, we have achieved an in-plane angular uncertainty of sigma(alpha) less than or similar to 40 degrees, and an out-of-plane uncertainty of sigma(beta) less than or similar to 30 degrees in each hemisphere. (C) 2011 Elsevier B.V. All rights reserved.

The emerging threats created by a global expansion of nuclear technologies and terrorism demand improved nuclear materials detection systems to aid in nuclear security and nonproliferation. This project develops the idea of using machine vision combined with a large-area gamma-ray imager to improve sensitivity to threats and their rapid localization in a crowded environment (e.g., subway stations, airports, and bridges). We have achieved our first coded-mask images with a 1 m 2 array of 100 NaI(Tl) detectors. In addition, two video cameras have been used in stereo to create a three-dimensional map of points in front of the array, and image segmentation is being implemented to distinguish and track individual objects in the field of view. We are currently gearing up to simultaneously perform real-time gamma-ray imaging and object tracking so that we can eventually merge the two data streams and achieve the expected increase in sensitivity of this method.

The massive earthquake and tsunami off the coast of Japan on March 11, 2011 caused extensive damage at the Fukushima Daiichi nuclear power plant. During subsequent venting and explosions at the reactor site, there were releases of fission products such as 131I, 134Cs, 136Cs, 137Cs, and 132Te. Trace amounts of these isotopes were detectable in California around March 17. In the days after the disaster, the Berkeley Radiological Air and Water Monitoring (BRAWM) Project was started to measure the amounts of radioisotopes in the local environment around Berkeley. BRAWM has detected radioactive isotopes from Fukushima Daiichi in the air, rainwater, creek runoff, milk, soil, berries, and leafy vegetables. The team continues to monitor fallout levels in order to understand the nature of the radioactive releases from Fukushima as well as quantify the dilution or accumulation of the radioisotopes as they make their way through the environment and food chain.