Bayesian mixture models for Poisson astronomical images

Details Bayesian mixture models for Poisson astronomical images Bayesian mixture models for Poisson astronomical images

2012-02-02

arxiv.org/abs/1202.0390v1

Astronomy and Astrophysics

Astrophysics

Instrumentation and Methods for Astrophysics

6 pages, 2 figures, invited talk at SCMA V, Penn State University, June 2011, PA. To appear in the Proceedings of "Statistical

Scientific paper

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.

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