This report describes how random pileup calculations are performed by the Gamma Detector Response and Analysis Software (GADRAS) Version 19.1. The computational approach and examples are presented for gamma-ray detectors with and without pileup rejectors. This pileup algorithm executes more quickly and the results are more accurate than previous versions of GADRAS. The detector response function can be refined to characterize distortions in peak shapes that occur at high-count rates. The empirical refinement can also be applied to describe the response of partially-effective pileup rejectors. Implications are discussed for the analysis of both static measurements and dynamic collections of the type acquired with radiation portals. ACKNOWLEDGEMENTS This work was funded by the Defense Threat Reduction Agency (DTRA) and the Department of Homeland Security (DHS) Counter Weapons of Mass Destruction (CWMD) office.
The Gamma Detector Response and Analysis Software (GADRAS) was augmented to enable empirical refinement of the Detector Response Function (DRF) for gamma-ray detectors. This capability is included in GADRAS starting with Version 18.8.2, which was released in February 2019 Empirical refinement enables improved computational accuracy for gamma-ray spectra when detectors exhibit characteristics that are essentially unique to a particular sensor. This report discusses how to perform the empirical refinement, and examples are presented for the following scenarios where empirical refinement is appropriate: Large plastic scintillators, which are generally composed of polyvinyl toluene (PVT), often exhibit low-energy peaks that are not associated with incident gamma rays. Regardless of whether they are artifacts produced by poor light collection or pulse processing methods, accurate spectral synthesis requires replication of these features; Some detectors, particularly those using Silicon Photomultipliers (SiPM), exhibit energy shifts of Compton edges and escape peaks relative to the same scintillator material attached to a photomultiplier tube. Empirical refinement compensates for these effects, which derive from nonlinearities in the detector response; The change in the DRF as a function of displacement of the source relative to the detector axis cannot always be replicated adequately using only the few shielding parameters that are used by the response function. Empirical refinement enables improved accuracy for computation of spectra during linear transits or when the detector is close to a large radiation source. This is a particularly important consideration for collimated detectors; and, Gamma-ray imagers exhibit complex relationships between spectral response and spatial locations. Empirical refinement enables substantial improvement of the DRF accuracy for imagers.
The effect of shielding on ionizing photon radiation can be estimated using radiation transport simulations. This report covers the methodology and implementation of using Green's Functions to pre-compute this effect, which allows the radiation field exiting a variety of shielding configurations to be quickly computed. It also covers a weighting function that makes a relatively small pre-computed library applicable to a large variety of heterogeneous shields. The method enables rapid computation of the intensity versus energy for scattered radiation exiting a variety of shield materials and thicknesses without running a full transport simulation.
This document describes how gamma-ray imagers are characterized using the Gamma Detector Response and Analysis Software (GADRAS). The initial step of the characterization process entails definition of detector dimensions and estimation of a few parameters that are specific to gamma-ray imagers. Energy calibration and resolution parameters are then adjusted based on comparison between computed spectra and measurements for several calibration source. These steps are analogous to the way non-imaging spectrometers are characterized. The parameters are then refined by an empirical process to achieved good agreement between measured and computed spectra as functions of gamma-ray energy and angular group.
This report evaluates the relative performance of two directional gamma-ray spectrometers and processing algorithms that are used to construct images and spatially resolved spectra. Polaris, which was developed by H3D Inc., uses 18 pixelated CZT crystals to construct gamma-ray images in either Compton camera(CC) or coded aperture (CA) mode. The other sensor that is referenced in this report incorporates a commercial high-purity germanium based imager, called GeGI, with a coded aperture mask and processing software developed by Oak Ridge National Laboratory (ORNL). H3D and the University of Michigan provided several algorithms that can be used to process data collected by Polaris in CC mode. This evaluation compares the performance of these algorithms with a Directional Unfolded Source Term (DUST) approach developed by Sandia National Laboratories (SNL). DUST differs from the other algorithms because its primary objective is synthesis of spatially-resolved gamma ray spectra as opposed to image reconstruction.
A Directional Unfolded Source Term (DUST) algorithm was developed to enable improved spectral analysis capabilities using data collected by Compton cameras. Achieving this objective required modification of the detector response function in the Gamma Detector Response and Analysis Software (GADRAS). Experimental data that were collected in support of this work include measurements of calibration sources at a range of separation distances and cylindrical depleted uranium castings.
A Directional Unfolded Source Term (DUST) method was developed to compute directionally resolved gamma-ray source terms based on back-projection spectra synthesized by Compton Cameras. Spectral features in the unprocessed spectra are indistinct primarily because the rotational angles for the conical projections cannot be determined, so probability distributions are constructed from overlapping cones. The DUST method uses an angular response function to compute a covariance matrix, which is used to process count rates in back-projection spectra by linear regression to partition the gamma-rays among several spatial regions. This method was applied to analyze data collected by the Polaris detector during an evaluation that was conducted at Oak Ridge National Laboratory (ORNL). The evaluation includes measurements of calibration sources with angular separations ranging from 1° to more than 50°. Measurements were also performed for cylindrical depleted uranium castings and a 137Cs source inside a large polyethylene sphere. The DUST algorithm was able to differentiate gamma-rays emitted by 137Cs and 60Co when the sources were separated by less than 2°, but separation greater than 10° was required to isolate the 133Ba emission from gamma-rays emitted by the other sources. The computed source terms were consistent with emission profiles from the calibration sources and from models of the spatially-extended sources. Methods for viewing radiation profiles were also evaluated because user input is required to select spatial regions of interest.
Isotope identification algorithms that are contained in the Gamma Detector Response and Analysis Software (GADRAS) can be used for real-time stationary measurement and search applications on platforms operating under Linux or Android operating systems. Since the background radiation can vary considerably due to variations in naturally-occurring radioactive materials (NORM), spectral algorithms can be substantially more sensitive to threat materials than search algorithms based strictly on count rate. Specific isotopes or interest can be designated for the search algorithm, which permits suppression of alarms for non-threatening sources, such as such as medical radionuclides. The same isotope identification algorithms that are used for search applications can also be used to process static measurements. The isotope identification algorithms follow the same protocols as those used by the Windows version of GADRAS, so files that are created under the Windows interface can be copied directly to processors on fielded sensors. The analysis algorithms contain provisions for gain adjustment and energy linearization, which enables direct processing of spectra as they are recorded by multichannel analyzers. Gain compensation is performed by utilizing photo-peaks in background spectra. Incorporation of this energy calibration tasks into the analysis algorithm also eliminates one of the more difficult challenges associated with development of radiation detection equipment.
The Gamma Detector Response and Analysis Software--Detector Response Function (GADRAS-DRF) application computes the response of gamma-ray and neutron detectors to incoming radiation. This manual provides step-by-step procedures to acquaint new users with the use of the application. The capabilities include characterization of detector response parameters, plotting and viewing measured and computed spectra, analyzing spectra to identify isotopes, and estimating source energy distributions from measured spectra. GADRAS-DRF can compute and provide detector responses quickly and accurately, giving users the ability to obtain usable results in a timely manner (a matter of seconds or minutes).
The Gamma Detector Response and Analysis Software - Detector Response Function (GADRAS-DRF) application computes the response of gamma-ray and neutron detectors to incoming radiation. This manual provides step-by-step procedures to acquaint new users with the use of the application. The capabilities include characterization of detector response parameters, plotting and viewing measured and computed spectra, analyzing spectra to identify isotopes, and estimating source energy distributions from measured spectra. GADRAS-DRF can compute and provide detector responses quickly and accurately, giving users the ability to obtain usable results in a timely manner (a matter of seconds or minutes).
The Gamma Detector Response and Analysis Software (GADRAS) applies a Detector Response Function (DRF) to compute the output of gamma-ray and neutron detectors when they are exposed to radiation sources. The DRF is fundamental to the ability to perform forward calculations (i.e., computation of the response of a detector to a known source), as well as the ability to analyze spectra to deduce the types and quantities of radioactive material to which the detectors are exposed. This document describes how gamma-ray spectra are computed and the significance of response function parameters that define characteristics of particular detectors.