This report describes how random pileup calculaitons 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.
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.
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 sys- tems. Since the background radiation can vary considerably due to variations in natu- rally-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 radionu- clides. 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 lineariza- tion, which enables direct processing of spectra as they are recorded by multichannel analyzers. Gain compensation is performed by utilizing photopeaks 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 radia- tion 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.
The GADRAS Development project comprises several elements that are all related to the Detector Response Function (DRF), which is the core of GADRAS. An ongoing activity is implementing continuous improvements in the accuracy and versatility of the DRF. The ability to perform rapid computation of the response of gammaray detectors for 3-D descriptions of source objects and their environments is a good example of a recent utilization of this versatility. The 3-D calculations, which execute several orders of magnitude faster than competing techniques, compute the response as an extension of the DRF so the radiation transport problem is never solved explicitly, thus saving considerable computational time. Maintenance of the Graphic User Interface (GUI) and extension of the GUI to enable construction of the 3-D source models is included in tasking for the GADRAS Development project. Another aspect of this project is application of the isotope identification algorithms for search applications. Specifically, SNL is tasked with development of an isotope-identification based search capability for use with the RSL-developed AVID system, which supports simultaneous operation of numerous radiation search assets. A Publically Available (PA) GADRAS-DRF application, which eliminates sensitive analysis components, will soon be available so that the DRF can be used by researchers at universities and corporations.
A method is presented that ascribes proper statistical variability to simulations that are derived from longer-duration measurements. This method is applicable to simulations of either real-value or integer-value data. An example is presented that demonstrates the applicability of this technique to the synthesis of gamma-ray spectra.
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 sys-tems. Since the background radiation can vary considerably due to variations in natu-rally-occurring radioactive materials (NORM), spectral algorithms can be substantial-ly 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 ap-plications 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 direct-ly to processors on fielded sensors. The analysis algorithms contain provisions for gain adjustment and energy lineariza-tion, which enables direct processing of spectra as they are recorded by multichannel analyzers. Gain compensation is performed by utilizing photopeaks 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 radia-tion detection equipment.
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.