Nuclear Technology / Volume 175 / Number 1 / July 2011 / Pages 326-334
Technical Paper / Special Issue on the 16th Biennial Topical Meeting of the Radiation Protection and Shielding Division / Radiation Measurements and General Instrumentation / 10.13182/NT10-72
Locating illicit radiological sources using gamma-ray or neutron detection is a key challenge for both homeland security and nuclear nonproliferation. Localization methods using an array of detectors or a sequence of observations in time and space must provide rapid results while accounting for a dynamic attenuating environment. In the presence of significant attenuation and scatter, more extensive numerical transport calculations in place of the standard analytical approximations may be required to achieve accurate results. Numerical adjoints based on deterministic transport codes provide relatively efficient detector response calculations needed to determine the most likely location of a true source given a set of observed count rates. Probabilistic representations account for uncertainty in the source location resulting from uncertainties in detector responses and the potential for nonunique solutions. A Bayesian approach improves on previous likelihood methods for source localization by allowing the incorporation of all available information to help constrain solutions.
We present an approach to localizing radiological sources that uses numerical adjoints and a Bayesian formulation and demonstrate the approach on two simple example scenarios. Results indicate accurate estimates of source locations. We briefly study the effect of neglecting the contribution of all scattered radiation in the adjoints, as analytical transport approximations do, for a case with moderately attenuating material between detectors and sources. The source location accuracy of the uncollided-only solutions appears to be significantly worse at the source strength considered here, suggesting that the higher physical fidelity that is provided by full numerical adjoint-based solutions may provide an advantage in operational settings.