This report summarizes GADRAS methods and gamma spectrometry rodeo uranium isotopic analysis results for high energy resolution H3D M400 cadmium zinc telluride (CZT) and ORTEC Micro Detective high-purity germanium (HPGe) spectra of uranium isotopic standards collected at Oak Ridge National Laboratory (ORNL) and Lawrence Livermore National Laboratory (LLNL) over a two-year measurement campaign. During the campaign, measurements were performed with the detectors unshielded, side shielded, and collimated. In addition, measurements of the uranium isotopic standards were performed unshielded and shielded.
Independent gamma spectroscopy data analysis of a plutonium oxide sample was requested on July 23, 2021. The primary request was to assess the Pu-239 activity/mass of the sample using previously collected gamma spectral data. Using the provided gamma spectral analysis report and spectral files, an independent evaluation of the data was conducted without any prior knowledge of the isotopic activity/mass of the sample.
Gamma Detector Response and Analysis Software (GADRAS) is used by the radiation detection and emergency response community to perform modeling and spectral analysis for gamma detector systems. Built into GADRAS is the ability to define a detector, geometry, background characteristics and source composition to generate synthetic spectra for drills and exercises (injects). Consequence Management is currently in development of a sample result data simulator tool in which a deposition model is probed for source conditions at moments in time and locations in space. These values are used to generate realistic sample results for use in drills and exercises. In addition to sample results, there is a need to simulate the actual spectra that would be observed in the field by downlooking HPGe instruments given a deposition activity. This way, the FRMAC Gamma Spectroscopist can practice their process of generating quantified results from spectra on realistic data as well. Recognizing the decades of work done in GADRAS to accurately generate synthetic spectra, this team decided to build a link between the new simulator and GADRAS to generate these spectra quickly and easily. The simulator tool will generate a file that specifies the name of the spectra, its location, date/time of measurement, duration of measurement, height off the ground, and the deposition activity and age for every radionuclide in the simulation. Then, a new tool within the Inject Tab of GADRAS was developed to read in this file given a detector selection and generate In-Situ spectra for each row in the file in any file format the user chooses. This way, simulation cell staff can take these files and then upload them to the appropriate data system (RAMS or RadResponder) for use during drills and exercises. An advanced feature of this tool allows for generating any spectra given an appropriate model and mapping of source to model layer in the batch inject tool. This way, spectra from field sample counts, mobile laboratories, or even fixed laboratories can be generated in bulk given an estimate of the radioactivity concentration or total radioactivity in an import file. This expands the capabilities of this tool a great deal and will make it a more useful tool for CM and others to help estimate detector response for nearly any situation. This user guide will explain the steps needed to perform a batch inject file generation.
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.
The goal of the Domestic Nuclear Detection Office (DNDO) Algorithm Improvement Program (AIP) is to facilitate gamma-radiation detector nuclide identification algorithm development, improvement, and validation. Accordingly, scoring criteria have been developed to objectively assess the performance of nuclide identification algorithms. In addition, a Microsoft Excel spreadsheet application for automated nuclide identification scoring has been developed. This report provides an overview of the equations, nuclide weighting factors, nuclide equivalencies, and configuration weighting factors used by the application for scoring nuclide identification algorithm performance. Furthermore, this report presents a general overview of the nuclide identification algorithm scoring application including illustrative examples.
The goal of the Domestic Nuclear Detection Office (DNDO) Algorithm Improvement Program (AIP) is to facilitate gamma-radiation detector nuclide identification algorithm development, improvement, and validation. Accordingly, scoring criteria have been developed to objectively assess the performance of nuclide identification algorithms. In addition, a Microsoft Excel spreadsheet application for automated nuclide identification scoring has been developed. This report provides an overview of the equations, nuclide weighting factors, nuclide equivalencies, and configuration weighting factors used by the application for scoring nuclide identification algorithm performance. Furthermore, this report presents a general overview of the nuclide identification algorithm scoring application including illustrative examples.
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.