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.
Radiation transport calculations were performed to compute the angular tallies for scattered gamma-rays as a function of distance, height, and environment. Greens Functions were then used to encapsulate the results a reusable transformation function. The calculations represent the transport of photons throughout scattering surfaces that surround sources and detectors, such as the ground and walls. Utilization of these calculations in GADRAS (Gamma Detector Response and Analysis Software) enables accurate computation of environmental scattering for a variety of environments and source configurations. This capability, which agrees well with numerous experimental benchmark measurements, is now deployed with GADRAS Version 18.2 as the basis for the computation of scattered radiation.
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).
T to compute the radiography properties of various materials, the flux profiles of X-ray sources must be characterized. This report describes the characterization of X-ray beam profiles from a Kimtron industrial 450 kVp radiography system with a Comet MXC-45 HP/11 bipolar oil-cooled X-ray tube. The empirical method described here uses a detector response function to derive photon flux profiles based on data collected with a small cadmium telluride detector. The flux profiles are then reduced to a simple parametric form that enables computation of beam profiles for arbitrary accelerator energies.
The Gamma Detector Response and Analysis Software (GADRAS) software package is capable of simulating the radiation transport physics for one-dimensional models. Spherical shells are naturally one-dimensional, and have been the focus of development and benchmarking. However, some objects are not spherical in shape, such as cylinders and boxes. These are not one-dimensional. Simulating the radiation transport in two or three dimensions is unattractive because of the extra computation time required. To maintain computational efficiency, higher-dimensional geometries require approximations to simulate them in one-dimension. This report summarizes the theory behind these approximations, tests the theory against other simulations, and compares the results to experimental data. Based on the results, it is recommended that GADRAS users always attempt to approximate reality using spherical shells. However, if fissile material is present, it is imperative that the shape of the one-dimensional model matches the fissile material, including the use of slab and cylinder geometry.
A common source of neutrons for calibration and testing is alpha-neutron material, named for the alpha-neutron nuclear reaction that occurs within. This material contains a long-lived alpha-emitter and a lighter target element. When the alpha particle from the emitter is absorbed by the target, neutrons and gamma rays are released. Gamma Detector Response and Analysis Software (GADRAS) includes built-in alpha-neutron source definitions for AcC, AmB, AmBe, AmF, AmLi, CmC, and PuC. In addition, GADRAS users may create their own alpha-neutron sources by placing valid alpha-emitters and target elements in materials within their one-dimensional models (1DModel). GADRAS has the ability to use pre-built alpha-neutron sources for plotting or as trace-sources in 1D models. In addition, if any material (existing or user-defined) specified in a 1D model contains both an alpha emitter in conjunction with a target nuclide, or there is an interface between such materials, then the appropriate neutron-emission rate from the alpha-neutron reaction will be computed. The gamma-emissions from these sources are also computed, but are limited to a subset of nine target nuclides. If a user has experimental data to contribute to the alpha-neutron gamma emission database, it may be added directly or submitted to the GADRAS developers for inclusion. The gadras.exe.config file will be replaced when GADRAS updates are installed, so sending the information to the GADRAS developers is the preferred method for updating the database. This is also preferable because it enables other users to benefit from your efforts.
This document is a final report for the polyvinyl toluene (PVT) neutron-gamma (PVT-NG) project, which was sponsored by the Domestic Nuclear Detection Office (DNDO). The PVT-NG sensor uses PVT detectors for both gamma and neutron detection. The sensor exhibits excellent spectral resolution and gain stabilization, which are features that are beneficial for detection of both gamma-ray and neutron sources. In fact, the ability to perform isotope identification based on spectra that were measured by the PVT-NG sensor was demonstrated. As described in a previous report, the neutron sensitivity of the first version of the prototype was about 25% less than the DNDO requirement of 2.5 cps/ng for bare Cf-252. This document describes design modifications that were expected to improve the neutron sensitivity by about 50% relative to the PVT-NG prototype. However, the project was terminated before execution of the design modifications after portal vendors demonstrated other technologies that enable neutron detection without the use of He-3. Nevertheless, the PVT-NG sensor development demonstrated several performance goals that may be useful in future portal designs.
Radiation portals normally incorporate a dedicated neutron counter and a gamma-ray detector with at least some spectroscopic capability. This paper describes the design and presents characterization data for a detection system called PVT-NG, which uses large polyvinyl toluene (PVT) detectors to monitor both types of radiation. The detector material is surrounded by polyvinyl chloride (PVC), which emits high-energy gamma rays following neutron capture reactions. Assessments based on high-energy gamma rays are well suited for the detection of neutron sources, particularly in border security applications, because few isotopes in the normal stream of commerce have significant gamma ray yields above 3 MeV. Therefore, an increased count rate for high-energy gamma rays is a strong indicator for the presence of a neutron source. The sensitivity of the PVT-NG sensor to bare {sup 252}Cf is 1.9 counts per second per nanogram (cps/ng) and the sensitivity for {sup 252}Cf surrounded by 2.5 cm of polyethylene is 2.3 cps/ng. The PVT-NG sensor is a proof-of-principal sensor that was not fully optimized. The neutron detector sensitivity could be improved, for instance, by using additional moderator. The PVT-NG detectors and associated electronics are designed to provide improved resolution, gain stability, and performance at high-count rates relative to PVT detectors in typical radiation portals. As well as addressing the needs for neutron detection, these characteristics are also desirable for analysis of the gamma-ray spectra. Accurate isotope identification results were obtained despite the common impression that the absence of photopeaks makes data collected by PVT detectors unsuitable for spectroscopic analysis. The PVT detectors in the PVT-NG unit are used for both gamma-ray and neutron detection, so the sensitive volume exceeds the volume of the detection elements in portals that use dedicated components to detect each type of radiation.
Variance estimates that are used in the analysis of radiation measurements must represent all of the measurement and computational uncertainties in order to obtain accurate parameter and uncertainty estimates. This report describes an approach for estimating components of the variance associated with both statistical and computational uncertainties. A multi-sensor fusion method is presented that renders parameter estimates for one-dimensional source models based on input from different types of sensors. Data obtained with multiple types of sensors improve the accuracy of the parameter estimates, and inconsistencies in measurements are also reflected in the uncertainties for the estimated parameter. Specific analysis examples are presented that incorporate a single gross neutron measurement with gamma-ray spectra that contain thousands of channels. The parameter estimation approach is tolerant of computational errors associated with detector response functions and source model approximations.
Sandia National Laboratories has developed an inverse radiation transport solver that applies nonlinear regression to coupled neutron-photon deterministic transport models. The inverse solver uses nonlinear regression to fit a radiation transport model to gamma spectrometry and neutron multiplicity counting measurements. The subject of this paper is the experimental validation of that solver. This paper describes a series of experiments conducted with a 4.5 kg sphere of {alpha}-phase, weapons-grade plutonium. The source was measured bare and reflected by high-density polyethylene (HDPE) spherical shells with total thicknesses between 1.27 and 15.24 cm. Neutron and photon emissions from the source were measured using three instruments: a gross neutron counter, a portable neutron multiplicity counter, and a high-resolution gamma spectrometer. These measurements were used as input to the inverse radiation transport solver to evaluate the solver's ability to correctly infer the configuration of the source from its measured radiation signatures.
Forward radiation transport is the problem of calculating the radiation field given a description of the radiation source and transport medium. In contrast, inverse transport is the problem of inferring the configuration of the radiation source and transport medium from measurements of the radiation field. As such, the identification and characterization of special nuclear materials (SNM) is a problem of inverse radiation transport, and numerous techniques to solve this problem have been previously developed. The authors have developed a solver based on nonlinear regression applied to deterministic coupled neutron-photon transport calculations. The subject of this paper is the experimental validation of that solver. This paper describes a series of experiments conducted with a 4.5-kg sphere of alpha-phase, weapons-grade plutonium. The source was measured in six different configurations: bare, and reflected by high-density polyethylene (HDPE) spherical shells with total thicknesses of 1.27, 2.54, 3.81, 7.62, and 15.24 cm. Neutron and photon emissions from the source were measured using three instruments: a gross neutron counter, a portable neutron multiplicity counter, and a high-resolution gamma spectrometer. These measurements were used as input to the inverse radiation transport solver to characterize the solver's ability to correctly infer the configuration of the source from its measured signatures.
The performance of the Gamma Detector Response and Analysis Software (GADRAS) was validated by comparing GADRAS model results to experimental measurements for a series of benchmark sources. Sources for the benchmark include a plutonium metal sphere, bare and shielded in polyethylene, plutonium oxide in cans, a highly enriched uranium sphere, bare and shielded in polyethylene, a depleted uranium shell and spheres, and a natural uranium sphere. The benchmark experimental data were previously acquired and consist of careful collection of background and calibration source spectra along with the source spectra. The calibration data were fit with GADRAS to determine response functions for the detector in each experiment. A one-dimensional model (pie chart) was constructed for each source based on the dimensions of the benchmark source. The GADRAS code made a forward calculation from each model to predict the radiation spectrum for the detector used in the benchmark experiment. The comparisons between the GADRAS calculation and the experimental measurements are excellent, validating that GADRAS can correctly predict the radiation spectra for these well-defined benchmark sources.
The primary function of the Gamma Detector Response and Analysis Software (GADRAS) is the solution of inverse radiation transport problems, by which the con-figuration of an unknown radiation source is inferred from one or more measured radia-tion signatures. GADRAS was originally developed for the analysis of gamma spec-trometry measurements. During fiscal years 2007 and 2008, GADRAS was augmented to implement the simultaneous analysis of neutron multiplicity measurements. This report describes the radiation transport methods developed to implement this new capability. This work was performed at the direction of the National Nuclear Security Administration's Office of Nonproliferation Research and Development. It was executed as an element of the Proliferation Detection Program's Simulation, Algorithm, and Modeling element. Acronyms BNL Brookhaven National Laboratory CSD Continuous Slowing-Down DU depleted uranium ENSDF Evaluated Nuclear Structure Data Files GADRAS Gamma Detector Response and Analysis Software HEU highly enriched uranium LANL Los Alamos National Laboratory LLNL Lawrence Livermore National Laboratory NA-22 Office of Nonproliferation Research and Development NNDC National Nuclear Data Center NNSA National Nuclear Security Administration ODE ordinary differential equation ONEDANT One-dimensional diffusion accelerated neutral particle transport ORNL Oak Ridge National Laboratory PARTISN Parallel time-dependent SN PDP Proliferation Detection Program RADSAT Radiation Scenario Analysis Toolkit RSICC Radiation Safety Information Computational Center SAM Simulation, Algorithms, and Modeling SNL Sandia National Laboratories SNM special nuclear material ToRI Table of Radioactive Isotopes URI uniform resource identifier XML Extensible Markup Language
The performance of the Advanced Synthetically Enhanced Detector Resolution Algorithm (ASEDRA) was evaluated by performing a blind test of 29 sets of gamma-ray spectra that were provided by DNDO. ASEDRA is a post-processing algorithm developed at the Florida Institute of Nuclear Detection and Security at the University of Florida (UF/FINDS) that extracts char-acteristic peaks in gamma-ray spectra. The QuickID algorithm, also developed at UF/FINDS, was then used to identify nuclides based on the characteristic peaks generated by ASEDRA that are inferred from the spectra. The ASEDRA/QuickID analysis results were evaluated with respect to the performance of the DHSIsotopeID algorithm, which is a mature analysis tool that is part of the Gamma Detector Response and Analysis Software (GADRAS). Data that were used for the blind test were intended to be challenging, and the radiation sources included thick shields around the radioactive materials as well as cargo containing naturally occurring radio-active materials, which masked emission from special nuclear materials and industrial isotopes. Evaluation of the analysis results with respect to the ground truth information (which was provided after the analyses were finalized) showed that neither ASEDRA/QuickID nor GADRAS could identify all of the radiation sources correctly. Overall, the purpose of this effort was primarily to evaluate ASEDRA, and GADRAS was used as a standard against which ASEDRA was compared. Although GADRAS was somewhat more accurate on average, the performance of ASEDRA exceeded that of GADRAS for some of the unknowns. The fact that GADRAS also failed to identify many of the radiation sources attests to the difficulty of analyzing the blind-test data that were used as a basis for the evaluation. This evaluation identified strengths and weaknesses of the two analysis approaches. The importance of good calibration data was also clear because the performance of both analysis methods was impeded by the inability to define the energy calibration accurately. Acronyms ACHIP adaptive chi-processed ASEDRA Advanced Synthetically Enhanced Detector Resolution Algorithm DNDO Domestic Nuclear Detection Office DRFs Detector Response Functions FINDS Florida Institute of Nuclear Detection and Security FWHM full-width half-maximum GADRAS Gamma Detector Response Analysis Software GUI graphical user interface HEU highly enriched uranium HPGe high purity germanium ID identification NaI Sodium iodide NNSA National Nuclear Security Administration NORM Naturally Occurring Radioactive Materials ppm parts per million SNL Sandia National Laboratories UF University of Florida WGPu weapons-grade plutonium