A difference boosting neural network for automated star-galaxy classification

Philip, N. S. ; Wadadekar, Y. ; Kembhavi, A. ; Joseph, K. B. (2002) A difference boosting neural network for automated star-galaxy classification Astronomy & Astrophysics, 385 (3). pp. 1119-1126. ISSN 0004-6361

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Official URL: http://www.aanda.org/articles/aa/abs/2002/15/aa143...

Related URL: http://dx.doi.org/10.1051/0004-6361:20020219

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.

Item Type:Article
Source:Copyright of this article belongs to European Southern Observatory.
Keywords:Galaxies: Fundamental Parameters; Stars: Fundamental Parameters; Methods: Statistical; Methods: Data Analysis
ID Code:25420
Deposited On:06 Dec 2010 13:25
Last Modified:17 May 2016 08:54

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