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 137Cs and 60Co gamma sources in an empty room. This test problem is solved using synthetic measurements generated by a Monte Carlo (MCNP) 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.
|Advisor:||Azmy, Yousry Y.|
|Commitee:||Anistratov, Dmitry Y., Kelly, C. T., Mattingly, John|
|School:||North Carolina State University|
|School Location:||United States -- North Carolina|
|Source:||DAI-B 74/07(E), Dissertation Abstracts International|
|Keywords:||Bayesian inference, Inverse problems, Radiation transport, Source mapping|
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