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The detection of radioactive contraband is a critical problem in maintaining national security for any country. Gamma-ray emissions from threat materials challenge both detection and measurement technologies significantly. The development of a sequential, model-based Bayesian processor that captures both the underlying transport physics of gamma-ray emissions including Compton scattering and the measurement of photon energies offers a physics-based approach to attack this challenging problem. The inclusion of a basic radionuclide representation of absorbed/scattered photons at a given energy along with interarrival times is used to extract the physics information available from noisy measurements. It is shown that this representation leads to an “extended” physics-based structure that can be used to develop an effective sequential detection technique. The resulting model-based processor is applied to data obtained from a controlled experiment in order to assess its feasibility.