Machine learning techniques to select variable stars

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 compu...

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Main Authors:	García-Varela Alejandro, Pérez Muriel, Sabogal Beatriz, Quiroz Adolfo
Format:	Article
Language:	English
Published:	EDP Sciences 2017-01-01
Series:	EPJ Web of Conferences
Online Access:	https://doi.org/10.1051/epjconf/201715203011

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spelling	doaj-0b60a19809524e9db8b041735cf1598a2021-08-02T09:35:19ZengEDP SciencesEPJ Web of Conferences2100-014X2017-01-011520301110.1051/epjconf/201715203011epjconf_puls2017_03011Machine learning techniques to select variable starsGarcía-Varela Alejandro0Pérez MurielSabogal Beatriz1Quiroz Adolfo2Universidad de los Andes, Departamento de FísicaUniversidad de los Andes, Departamento de FísicaUniversidad de los Andes, Departamento de MatemáticasIn 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.https://doi.org/10.1051/epjconf/201715203011
collection	DOAJ
language	English
format	Article
sources	DOAJ
author	García-Varela Alejandro Pérez Muriel Sabogal Beatriz Quiroz Adolfo
spellingShingle	García-Varela Alejandro Pérez Muriel Sabogal Beatriz Quiroz Adolfo Machine learning techniques to select variable stars EPJ Web of Conferences
author_facet	García-Varela Alejandro Pérez Muriel Sabogal Beatriz Quiroz Adolfo
author_sort	García-Varela Alejandro
title	Machine learning techniques to select variable stars
title_short	Machine learning techniques to select variable stars
title_full	Machine learning techniques to select variable stars
title_fullStr	Machine learning techniques to select variable stars
title_full_unstemmed	Machine learning techniques to select variable stars
title_sort	machine learning techniques to select variable stars
publisher	EDP Sciences
series	EPJ Web of Conferences
issn	2100-014X
publishDate	2017-01-01
description	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.
url	https://doi.org/10.1051/epjconf/201715203011
work_keys_str_mv	AT garciavarelaalejandro machinelearningtechniquestoselectvariablestars AT perezmuriel machinelearningtechniquestoselectvariablestars AT sabogalbeatriz machinelearningtechniquestoselectvariablestars AT quirozadolfo machinelearningtechniquestoselectvariablestars
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Machine learning techniques to select variable stars

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