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
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
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.