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SExtractor: Software for source extraction

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Bertin, E., Arnouts, S.·1996·Astronomy and Astrophysics Supplement Series

Abstract

We present the automated techniques we have developed for new software that optimally detects, deblends, 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.

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Metadata

Authors
Bertin, E., & Arnouts, S.
Venue
Astronomy and Astrophysics Supplement Series
Publisher
EDP Sciences
Published
1996-06
Volume
117
Issue
2
Pages
393-404
ISSN
  • 0365-0138 (ISSN-L)
  • 1286-4846
OpenAlex
W2142238829
Cited by
10,866
References
0
FWCI
44.24
Type
journal article
Language
en
Open access
bronze

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CoChat. (2026). Citation check: SExtractor: Software for source extraction. https://app.cochat.ai/papers/10.1051/aas:1996164
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Bertin, E., & Arnouts, S. (1996). SExtractor: Software for source extraction. Astronomy and Astrophysics Supplement Series. https://doi.org/10.1051/aas%3A1996164

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Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.