A new scatter calculation algorithm has been implemented in the Gamma Detector Response and Analysis Software (GADRAS) package that accounts for spectral effects of scattering materials not in the line of sight of the detector and the source. Previously, GADRAS would only apply scattering effects due to materials that fall between the source and detector. This new routine will allow better modeling of various scenarios including gamma imagers, collimated detectors, or traditional gamma detectors where scattering materials are present.
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.
The Gamma Detector Response and Analysis Software (GADRAS) team has enhanced the source term input for the creation of synthetic spectra. These enhancements include the following: allowing users to programmatically provide source information to GADRAS through memory, rather than through a string limited to 256 characters; allowing users to provide their own source decay database information; and updating the default GADRAS decay database to fix errors and include coincident gamma information.
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.
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.
Simulating gamma spectra is useful for analyzing special nuclear materials. Gamma spectra are influenced not only by the source and the detector, but also by the external, and potentially complex, scattering environment. The scattering environment can make accurate representations of gamma spectra difficult to obtain. By coupling the Monte Carlo Nuclear Particle (MCNP) code with the Gamma Detector Response and Analysis Software (GADRAS) detector response function, gamma spectrum simulations can be computed with a high degree of fidelity even in the presence of a complex scattering environment. Traditionally, GADRAS represents the external scattering environment with empirically derived scattering parameters. By modeling the external scattering environment in MCNP and using the results as input for the GADRAS detector response function, gamma spectra can be obtained with a high degree of fidelity. This method was verified with experimental data obtained in an environment with a significant amount of scattering material. The experiment used both gamma-emitting sources and moderated and bare neutron-emitting sources. The sources were modeled using GADRAS and MCNP in the presence of the external scattering environment, producing accurate representations of the experimental data.
The Gamma Detector Response and Analysis Software-Detector Response Function (GADRAS-DRF) application computes the response of gamma-ray 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, and analyzing spectra to identify isotopes or to estimate flux profiles. GADRAS-DRF can compute and provide detector responses quickly and accurately, giving researchers and other users the ability to obtain usable results in a timely manner (a matter of seconds or minutes).