Speaker
Description
Designing spectroscopic follow-up observations in astronomy poses several challenges. Observing spectra is significantly more time consuming than photometric imaging observations yet, interesting objects need to be selected based on images taken with only a few broad-band filters. Hierarchical Bayesian Networks are often used to estimate physical parameters of photometrically observed stars, a prerequisite to successful spectroscopic targeting. We present an implementation of a novel Bayesian model which can be used to fit parameters of mixtures of stellar populations to derive physical parameters as well as population membership probabilities for each star. Since the Adaptive Monte Carlo method used to integrate the model is computationally expensive, we rely heavily on GPUs.
