| Issue |
EPJ Web Conf.
Volume 152, 2017
Wide-Field Variability Surveys: A 21st Century Perspective – 22nd Los Alamos Stellar Pulsation – Conference Series Meeting
|
|
|---|---|---|
| Article Number | 03011 | |
| Number of page(s) | 2 | |
| Section | Statistical challenges, new approaches and techniques | |
| DOI | https://doi.org/10.1051/epjconf/201715203011 | |
| Published online | 08 September 2017 | |
https://doi.org/10.1051/epjconf/201715203011
Machine learning techniques to select variable stars
1 Universidad de los Andes, Departamento de Física, Cra.1 No.18A-10, Edificio Ip, Bogotá, Colombia
2 Universidad de los Andes, Departamento de Matemáticas, Cra.1 No.18A-10, Edificio H, Bogotá, Colombia
* josegarc@uniandes.edu.co
** mf.perez648@uniandes.edu.co
*** bsabogal@uniandes.edu.co
**** aj.quiroz1079@uniandes.edu.co
Published online: 8 September 2017
In order to perform a supervised classification of variable stars, we propose and evaluate a set of six features extracted from the magnitude density of the light curves. They are used to train automatic classification systems using state-of-the-art classifiers implemented in the R statistical computing environment. We find that random forests is the most successful method to select variables.
© The Authors, published by EDP Sciences, 2017
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