Meta Classification for Variable Stars

TL;DR

This paper introduces a meta-model that combines existing variable star classification models to improve accuracy and flexibility in astronomical data analysis, reducing the need for retraining from scratch.

Contribution

The paper presents a novel meta-classification approach that integrates diverse models trained on different data representations and tasks, addressing a key challenge in astronomical data analysis.

Findings

Meta-model effectively combines existing classifiers for variable stars.

Reduces computational costs by selectively using complex models.

Achieves high classification accuracy on EROS-2 and MACHO datasets.

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.