Volume 446, Number 1, January IV 2006
|Page(s)||395 - 402|
|Published online||09 January 2006|
Automatic classification of eclipsing binaries light curves using neural networks
Dpt. de Inteligencia Artificial, U.N.E.D., c/ Juan del Rosal, 16, 28040 Madrid, Spain e-mail: email@example.com
2 Laboratorio de Astrofísica Espacial y Física Fundamental, PO Box 50727, 28080 Madrid, Spain
3 XMM-Newton SOC, ESAC, PO Box 50727, 28080 Madrid, Spain e-mail: Celia.Sanchez@sciops.esa.int
4 Research and Scientific Support Department, ESA, ESTEC, Postbus 299, 2200 AG Noordwijk, The Netherlands e-mail: firstname.lastname@example.org
Accepted: 26 August 2005
In this work we present a system for the automatic classification of the light curves of eclipsing binaries. This system is based on a classification scheme that aims to separate eclipsing binary systems according to their geometrical configuration in a modified version of the traditional classification scheme. The classification is performed by a Bayesian ensemble of neural networks trained with Hipparcos data of seven different categories including eccentric binary systems and two types of pulsating light curve morphologies.
Key words: stars: binaries: eclipsing / stars: binaries: general / methods: data analysis / methods: statistical
© ESO, 2006
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.