John Mattingly

A Framework for the Solution of Inverse Radiation Transport Problems

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 the source 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.

A framework for the solution of inverse radiation transport problems

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 source terms and transport medium caused that observation? This paper presents a framework for solving inverse radiation transport problems, describes its essential components, and illustrates its features and performance. The framework implements an implicit solution to the inverse transport problem using deterministic neutron, electron, and photon transport calculations embedded in a Levenberg-Marquardt nonlinear optimization solver. The solver finds the layer thicknesses of a one-dimensional transport model by minimizing the difference between the gamma spectrum calculated by deterministic transport and the measured gamma spectrum. The fit to the measured spectrum is a full-spectrum analysis-all spectral features are modeled, including photopeaks and continua from spontaneous and induced photon emissions. An example problem is solved by analyzing a high-resolution gamma spectrometry measurement of plutonium metal.

Benchmarks for GADRAS performance validation.

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.

Computation of neutron multiplicity statistics using deterministic transport

Nuclear nonproliferation efforts are supported by measurements that are capable of rapidly characterizing special nuclear materials (SNM). Neutron multiplicity counting is frequently used to estimate properties of SNM, including neutron source strength, multiplication, and generation time. Different classes of models have been used to estimate these and other properties from the measured neutron counting distribution and its statistics. This paper describes a technique to compute statistics of the neutron counting distribution using deterministic neutron transport models. This approach can be applied to rapidly analyze neutron multiplicity counting measurements without relying on the point reactor kinetics model.

Validation of MCNPX-PoliMi fission models

We present new results on the measurement of correlated, outgoing neutrons from spontaneous fission events in a Cf-252 source. 16 EJ-309 liquid scintillation detectors are used to measure neutron-neutron correlations for various detector angles. Anisotropy in neutron emission is observed. The results are compared to MCNPX-PoliMi simulations and good agreement is observed.

Plutonium sphere multiplicity simulations with MCNP-PoliMi

In order to improve the characterization methods for fissile materials, effort must be made to validate the computer codes that are used to simulate the behavior of these systems. For this work, measurements of a 4.4-kg sphere of weapons grade plutonium metal were taken. A detector array of 3He tubes embedded in polyethylene was used to measure the multiplicity of the system. The experiment was then modeled using the MCNP-PoliMi code and the simulated results were compared to the measured results using the Feynman-Y metric. MCNP-PoliMi is able to correctly predict the measured value of the Feynman-Y vale within 5% for all of the moderated cases and within 22% for the bare sphere.

Accounting for correlated errors in inverse radiation transport problems

Inverse radiation transport focuses on identifying the configuration of an unknown radiation source given its observed radiation signatures. The inverse problem is solved by finding the set of transport model variables that minimizes a weighted sum of the squared differences by channel between the observed signature and the signature predicted by the hypothesized model parameters. The weights per channel are inversely proportional to the sum of the variances of the measurement and model errors at a given channel. In the current treatment, the implicit assumption is that the errors (differences between the modeled and observed radiation signatures) are independent across channels. In this paper, an alternative method that accounts for correlated errors between channels is described and illustrated for inverse problems based on gamma spectroscopy.