Bayesian mixture models for Poisson astronomical images
Abstract
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.
Cite
@article{arxiv.1202.0390,
title = {Bayesian mixture models for Poisson astronomical images},
author = {Fabrizia Guglielmetti and Rainer Fischer and Volker Dose},
journal= {arXiv preprint arXiv:1202.0390},
year = {2012}
}
Comments
6 pages, 2 figures, invited talk at SCMA V, Penn State University, June 2011, PA. To appear in the Proceedings of "Statistical Challenges in Modern Astronomy V"