Mark S. Bandstra

Effects of Background on Gamma-Ray Detection for Mobile Spectroscopy and Imaging Systems

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.

CsI(Na) Detector Array Characterization for ARES Program

Researchers at Lawrence Berkeley National Laboratory have been supporting the Transformational and Applied Research Directorate in the Domestic Nuclear Detection Office of the Department of Homeland Security to define needs for, to develop, and to test a scintillator-based radiation detection and localization system to be fielded on a helicopter platform - the so-called Airborne Radiological Enhanced-sensor System. The system comprises an array of 92 CsI(Na) detectors that are arranged to function as an active mask to encode the directionality in the roll-dimension of measured gamma rays and is additionally capable of Compton imaging. Additional contextual sensors and specially-developed algorithms are also being fielded for characterization with the goal of detecting, localizing, and helping to interdict radiological and nuclear threats via airborne search. The algorithms that are being developed leverage contextual information including topography, geography, hyperspectral imagery, video tracking, and platform positioning. This paper describes recent characterization efforts of the CsI(Na) detector system including energy, position, and timing resolution and synchronization between the 184 individual photomultiplier tubes.

A Comparison of the Detection Sensitivity of the Poisson Clutter Split and Region of Interest Algorithms on the RadMAP Mobile System

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.

Multiagency Urban Search Experiment Detector and Algorithm Test Bed

In order to provide benchmark data sets for radiation detector and algorithm development, a particle transport test bed has been created using experimental data as model input and validation. A detailed radiation measurement campaign at the Combined Arms Collective Training Facility in Fort Indiantown Gap, PA (FTIG), USA, provides sample background radiation levels for a variety of materials present at the site (including cinder block, gravel, asphalt, and soil) using long dwell high-purity germanium (HPGe) measurements. In addition, detailed light detection and ranging data and ground-truth measurements inform model geometry. This paper describes the collected data and the application of these data to create background and injected source synthetic data for an arbitrary gamma-ray detection system using particle transport model detector response calculations and statistical sampling. In the methodology presented here, HPGe measurements inform model source terms while detector response calculations are validated via long dwell measurements using 2”

List-mode source injection algorithm for detectors with arbitrary pose and trajectory

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Web-based visual data exploration for improved radiological source detection

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Reconstruction of background radiation emissivity of urban structures using a truck-based detector array

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RadMAP: The Radiological Multi-sensor Analysis Platform

” NaI(Tl) detectors at a variety of measurement points. A collection of responses, along with sampling methods and interpolation, can be used to create data sets to gauge radiation detector and algorithm (including detection, identification, and localization) performance under a variety of scenarios. Data collected at the FTIG site are available for query, filtering, visualization, and download at muse.lbl.gov.

Measurement of the Energy-Dependent Angular Response of the ARES Detector System and Application to Aerial Imaging

When evaluating detectors and algorithms for nuclear threat detection in populated environments, introducing actual sources is usually neither feasible nor economical. It is more practical to move the detector, (which is either airborne, vehicle-borne, or human portable) through the test environment and then to later artificially superimpose either measured or simulated source signatures on the recorded background data. We present a source injection algorithm that can be used to inject either measured or simulated source data into list-mode background data. It is designed to accommodate cases where measured “injection data” are available only at a limited set of locations. We describe a sampling scheme suitable for obtaining measured source injection data. We then use these data to demonstrate the source injection algorithm applied to list-mode data collected with a helicopter-borne detector system, where arbitrary detector poses and trajectories are possible. Stochastic methods are used both to select source events from a dataset containing both source and background events, and to scale the number of selected events to match the field conditions of detector-source distance, air attenuation, acquisition duration, and the relative strength of the measured and injected sources. This algorithm is planned to be used as part of the Airborne Radiation Enhanced-sensor System (ARES) Advanced Technology Demonstration.

Impact of detector efficiency and energy resolution on gamma-ray background rejection in mobile spectroscopy and imaging systems

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 analysts use cases, and choosing input parameters for analysis. Downloading the data and exploring it on an analysts 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.