A difference boosting neural network for automated star-galaxy classification
Astrophysics
2009-11-07 v1
Abstract
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.
Cite
@article{arxiv.astro-ph/0202127,
title = {A difference boosting neural network for automated star-galaxy classification},
author = {Ninan Sajeeth Philip and Yogesh Wadadekar and Ajit Kembhavi and K. Babu Joseph},
journal= {arXiv preprint arXiv:astro-ph/0202127},
year = {2009}
}
Comments
9 pages, 1figure, 7 tables, accepted for publication in Astronomy and Astrophysics