Astronomy and Astrophysics – Astrophysics
Scientific paper
Jun 1996
adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=1996a%26as..117..393b&link_type=abstract
Astronomy and Astrophysics Supplement, v.117, p.393-404
Astronomy and Astrophysics
Astrophysics
3741
Methods: Data Analysis, Techniques: Image Processing, Galaxies: Photometry
Scientific paper
We present the automated techniques we have developed for new software that optimally detects, de-blends, measures and classifies sources from astronomical images: SExtractor (Source Extractor). We show that a very reliable star/galaxy separation can be achieved on most images using a neural network trained with simulated images. Salient features of SExtractor include its ability to work on very large images, with minimal human intervention, and to deal with a wide variety of object shapes and magnitudes. It is therefore particularly suited to the analysis of large extragalactic surveys.
Arnouts Stephane
Bertin Emmanuel
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