Volume 385, Number 3, April III 2002
|Page(s)||1119 - 1126|
|Section||Numerical methods and codes|
|Published online||15 April 2002|
A difference boosting neural network for automated star-galaxy classification
Cochin University of Science and Technology, Kochi – 682 022, India
2 Institut d'Astrophysique de Paris, 98bis Boulevard Arago, 75014 Paris, France
3 Inter University Center for Astronomy and Astrophysics, Post Bag 4, Ganeshkhind, Pune – 411 007, India
Corresponding author: Y. Wadadekar, email@example.com
Accepted: 5 February 2002
In this paper we describe the use of a new artificial neural network, called the difference boosting neural network (DBNN), for automated classification problems in astronomical data analysis. We illustrate the capabilities of the network by applying it to star galaxy classification using recently released, deep imaging data. We have compared our results with classification made by the widely used Source Extractor (SExtractor) package. We show that while the performance of the DBNN in star-galaxy classification is comparable to that of SExtractor, it has the advantage of significantly higher speed and flexibility during training as well as classification.
Key words: galaxies: fundamental parameters / stars: fundamental parameters / methods: statistical / methods: data analysis
© ESO, 2002
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