Bayesian mixture models for Poisson astronomical images
  Astronomical images in the Poisson regime are typically characterized by a
spatially varying cosmic background, large variety of source morphologies and
intensities, data incompleteness, steep gradients in the data, and few photon
counts per pixel. The Background-Source separation technique is developed with
the aim to detect faint and extended sources in astronomical images
characterized by Poisson statistics. The technique employs Bayesian mixture
models to reliably detect the background as well as the sources with their
respective uncertainties. Background estimation and source detection is
achieved in a single algorithm. A large variety of source morphologies is
revealed. The technique is applied in the X-ray part of the electromagnetic
spectrum on ROSAT and Chandra data sets and it is under a feasibility study for
the forthcoming eROSITA mission.
