A new approach to the optimization of the extraction of astrometric and photometric information from multi-wavelength images in cosmological fields
arXiv:1203.6052 · doi:10.1007/978-1-4614-3323-1_18
Abstract
This paper describes a new approach to the optimization of information extraction in multi-wavelength image cubes of cosmological fields. The objective is to create a framework for the automatic identification and tagging of sources according to various criteria (isolated source, partially overlapped, fully overlapped, cross-matched, etc) and to set the basis for the automatic production of the SEDs (spectral energy distributions) for all objects detected in the many multi-wavelength images in cosmological fields.In order to do so, a processing pipeline is designed that combines Voronoi tessellation, Bayesian cross-matching, and active contours to create a graph-based representation of the cross-match probabilities. This pipeline produces a set of SEDs with quality tags suitable for the application of already-proven data mining methods. The pipeline briefly described here is also applicable to other astrophysical scenarios such as star forming regions.
GREAT Workshop. This paper will be published in Springer as part of the proceedings for the GREAT Workshop