  In this investigation, we propose several algorithms to recover the location
and intensity of a radiation source located in a simulated 250 m x 180 m block
in an urban center based on synthetic measurements. Radioactive decay and
detection are Poisson random processes, so we employ likelihood functions based
on this distribution. Due to the domain geometry and the proposed response
model, the negative logarithm of the likelihood is only piecewise continuous
differentiable, and it has multiple local minima. To address these
difficulties, we investigate three hybrid algorithms comprised of mixed
optimization techniques. For global optimization, we consider Simulated
Annealing (SA), Particle Swarm (PS) and Genetic Algorithm (GA), which rely
solely on objective function evaluations; i.e., they do not evaluate the
gradient in the objective function. By employing early stopping criteria for
the global optimization methods, a pseudo-optimum point is obtained. This is
subsequently utilized as the initial value by the deterministic Implicit
Filtering method (IF), which is able to find local extrema in non-smooth
functions, to finish the search in a narrow domain. These new hybrid techniques
combining global optimization and Implicit Filtering address difficulties
associated with the non-smooth response, and their performances are shown to
significantly decrease the computational time over the global optimization
methods alone. To quantify uncertainties associated with the source location
and intensity, we employ the Delayed Rejection Adaptive Metropolis (DRAM) and
DiffeRential Evolution Adaptive Metropolis (DREAM) algorithms. Marginal
densities of the source properties are obtained, and the means of the chains'
compare accurately with the estimates produced by the hybrid algorithms.
