Meta Classification for Variable Stars

Abstract

The need for the development of automatic tools to explore astronomical databases has been recognized since the inception of CCDs and modern computers. Astronomers already have developed solutions to tackle several science problems, such as automatic classification of stellar objects, outlier detection, and globular clusters identification, among others. New science problems emerge and it is critical to be able to re-use the models learned before, without rebuilding everything from the beginning when the science problem changes. In this paper, we propose a new meta-model that automatically integrates existing classification models of variable stars. The proposed meta-model incorporates existing models that are trained in a different context, answering different questions and using different representations of data. Conventional mixture of experts algorithms in machine learning literature can not be used since each expert (model) uses different inputs. We also consider computational complexity of the model by using the most expensive models only when it is necessary. We test our model with EROS-2 and MACHO datasets, and we show that we solve most of the classification challenges only by training a meta-model to learn how to integrate the previous experts.

Accepted for publication, The Astrophysical Journal

References in corpus (7)

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• The EPOCH Project: I. Periodic variable stars in the EROS-2 LMC database
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• Non-Normal Mixtures of Experts

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• Automatic Survey-Invariant Variable Star Classification
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• The VVV Infrared Variability Catalog (VIVA-I)
• Uncertain classification of Variable Stars: handling observational GAPS and noise
• Machine learning techniques to select Be star candidates. An application in the OGLE-IV Gaia south ecliptic pole field
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• Predicting Stellar Rotation Periods Using XGBoost
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• Informative Bayesian model selection for RR Lyrae star classifiers
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