A Framework for the Solution of Inverse Radiation Transport Problems

Mattingly, Carolyn J.; Mitchell, Dean J.

doi:10.1109/tns.2010.2076371

Cited by 26 publications

(11 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…28 Mattingly and Mitchell tackled a one-dimensional spherical problem in which the material layer thicknesses were unknown but the materials were known, or could be inferred from the peaks present in the high-resolution detector reading. 77 The unknowns in the solution were the layer thicknesses, although the more fundamental unknowns seemed to be the total mass of each material present. To more tightly constrain the solution, Mattingly and his coauthors include more physics than others, using neutron multiplicity counting in addition to the more conventional gamma spectroscopy.…”

Section: Discrete Methodsmentioning

confidence: 99%

“…This is the approach taken in Refs. [10,77]. If the forward model can be represented by a mapping T α x = y(α), and the computed responses by an inner product r = c, x , where α is the unknown input, the typical algorithm is then:…”

Section: E2 Methodsmentioning

confidence: 99%

“…As the goal of this project is to develop a proof-of-principle of the proposed approach, meeting stringent operational constraints is not of primary concern at this stage. While some inverse transport problems require that the calculation complete in a second or two, 77 we are under no such constraints here. In addition, we take the liberty to use high-performance computing resources, where applicable, that are typically unavailable in the field.…”

Section: Computational Resources and Constraintsmentioning

confidence: 99%

“…44 Closely related is material identification, where a common goal is to detect illicit nuclear materials. 77 Given the wide range of ip applications and methods, there are a number of ways to classify and sort the methods. Algorithmically, the most important distinction is analytic or closed-form solutions versus discrete or implicit methods.…”

Section: Nverse Radiation Transport Problemsmentioning

confidence: 99%

“…In both the Favorite-Ketcheson paper and the Miller-Charlton paper, the authors use an adjoint to estimate these derivatives, 33,86 while Mattingly and Mitchell use the simpler finite-difference approximation. 77 In this test, we use two approaches. First, we can compute the Jacobian with respect to q since the problem is linear.…”

Section: E21 Basis Of the Source Reconstructionmentioning

confidence: 99%

See 4 more Smart Citations

Radiation Source Mapping with Bayesian Inverse Methods

Hykes

Azmy

2015

Nuclear Science and Engineering

View full text Add to dashboard Cite

HYKES, JOSHUA MICHAEL. Radiation Source Mapping with Bayesian Inverse Methods. (Under the direction of Yousry Y. Azmy.) We present a method to map the spectral and spatial distributions of radioactive sources using a small number of detectors. Locating and identifying radioactive materials is important for border monitoring, accounting for special nuclear material in processing facilities, and in clean-up operations. Most methods to analyze these problems make restrictive assumptions about the distribution of the source. In contrast, the source-mapping method presented here allows an arbitrary three-dimensional distribution in space and a flexible group and gamma peak distribution in energy. To apply the method, the system's geometry and materials must be known. A probabilistic Bayesian approach is used to solve the resulting inverse problem (ip) since the system of equations is ill-posed. The probabilistic approach also provides estimates of the confidence in the final source map prediction. A set of adjoint flux, discrete ordinates solutions, obtained in this work by the Denovo code, are required to efficiently compute detector responses from a candidate source distribution. These adjoint fluxes are then used to form the linear model to map the state space to the response space. The test for the method is simultaneously locating a set of 137 Cs and 60 Co gamma sources in an empty room. This test problem is solved using synthetic measurements generated by a Monte Carlo (m c n p) model and using experimental measurements that we collected for this purpose. With the synthetic data, the predicted source distributions identified the locations of the sources to within tens of centimeters, in a room with an approximately four-by-four meter floor plan. Most of the predicted source intensities were within a factor of ten of their true value. The chi-square value of the predicted source was within a factor of five from the expected value based on the number of measurements employed. With a favorable uniform initial guess, the predicted source map was nearly identical to the true distribution, and the source intensities agreed within the predicted uncertainty. Using experimental data, the mapping was more difficult due to laboratory limitations. However, by supplanting 14 flawed measurements (out of 69 total) with synthetic data, the proof-of-principle source mapping was nearly as accurate as the synthetic-only prediction.

show abstract

Section: Discrete Methodsmentioning

confidence: 99%

Section: E2 Methodsmentioning

confidence: 99%

Section: Computational Resources and Constraintsmentioning

confidence: 99%

Section: Nverse Radiation Transport Problemsmentioning

confidence: 99%

Section: E21 Basis Of the Source Reconstructionmentioning

confidence: 99%

See 3 more Smart Citations

Radiation Source Mapping with Bayesian Inverse Methods

Hykes

Azmy

2015

Nuclear Science and Engineering

View full text Add to dashboard Cite

show abstract

Survey of Machine Learning Approaches in Radiation Data Analytics Pertained to Nuclear Security

Alamaniotis

Heifetz

2021

Learning and Analytics in Intelligent Systems

View full text Add to dashboard Cite

The increasing concerns over the use of nuclear materials for malevolent purposes (i.e., terrorist attacks) have fueled the interest in developing technologies that can detect hidden nuclear material before its use. The process of detecting and identifying nuclear materials for non-reported purposes is under the umbrella of nuclear security. Among several areas that contribute to the security and safeguards of nuclear materials, radiation data analytics has recently been marked as an area of high potential. Therefore, there is an increasing trend in applying machine learning for developing data analysis methods applied to radiation signals aiming at identifying patterns of interest associated with nuclear materials. This chapter aspires in providing a comprehensive survey and discussion of machine learning and data analytics methods pertained to nuclear security. The chapter will also discuss further trends and how data analytics can further enhance nuclear security by effectively analyzing radiation data.

show abstract

Nuclear Security Science

LaGraffe¹

2018

Handbook of Security Science

View full text Add to dashboard Cite

A Framework for the Solution of Inverse Radiation Transport Problems

Cited by 26 publications

References 11 publications

Radiation Source Mapping with Bayesian Inverse Methods

Radiation Source Mapping with Bayesian Inverse Methods

Survey of Machine Learning Approaches in Radiation Data Analytics Pertained to Nuclear Security

Nuclear Security Science

Contact Info

Product

Resources

About