  The spectral energy distribution (SED) is a relatively easy way for
astronomers to distinguish between different astronomical objects such as
galaxies, black holes, and stellar objects. By comparing the observations from
a source at different frequencies with template models, astronomers are able to
infer the type of this observed object. In this paper, we take a Bayesian model
averaging perspective to learn astronomical objects, employing a Bayesian
nonparametric approach to accommodate the deviation from convex combinations of
known log-SEDs. To effectively use telescope time for observations, we then
study Bayesian nonparametric sequential experimental design without conjugacy,
in which we use sequential Monte Carlo as an efficient tool to maximize the
volume of information stored in the posterior distribution of the parameters of
interest. A new technique for performing inferences in log-Gaussian Cox
processes called the Poisson log-normal approximation is also proposed.
Simulations show the speed, accuracy, and usefulness of our method. While the
strategy we propose in this paper is brand new in the astronomy literature, the
inferential techniques developed apply to more general nonparametric sequential
experimental design problems.
