  The article addresses the problem of detecting presence and location of a
small low emission source inside of an object, when the background noise
dominates. This problem arises, for instance, in some homeland security
applications. The goal is to reach the signal-to-noise ratio (SNR) levels on
the order of $10^{-3}$. A Bayesian approach to this problem is implemented in
2D. The method allows inference not only about the existence of the source, but
also about its location. We derive Bayes factors for model selection and
estimation of location based on Markov Chain Monte Carlo (MCMC) simulation. A
simulation study shows that with sufficiently high total emission level, our
method can effectively locate the source.
