Publications

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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.

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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.

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Radiation sensing applications for SNM detection, identification, and characterization all face the same fundamental problem: each to varying degrees must infer the presence, identity, and configuration of a radiation source given a set of radiation signatures. This is a problem of inverse radiation transport: given the outcome of a measurement, what was thesource and transport medium that caused that observation? This paper presents a framework for solving inverse radiation transport problems, describes its essential components, and illustrates its features and performance. © 2008 IEEE.

The primary function of the Gamma Detector Response and Analysis Software (GADRAS) is the solution of inverse radiation transport problems, by which the configuration of an unknown radiation source is inferred from one or more measured radiation signatures. GADRAS was originally developed for the analysis of gamma spectrometry 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.

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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

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