Unsupervised Classification of Variable Stars

TL;DR

This paper introduces an unsupervised algorithm for classifying variable stars using light curve similarity, eliminating the need for labeled training data and enabling scalable analysis of large astronomical datasets.

Contribution

It presents a novel query-based unsupervised classification method and a fast similarity function tailored for light curves, addressing the challenge of limited labeled data.

Findings

High accuracy in classifying variable stars

Scales efficiently to large datasets

Operates without labeled training sets

Abstract

During the last ten years, a considerable amount of effort has been made to develop algorithms for automatic classification of variable stars. That has been primarily achieved by applying machine learning methods to photometric datasets where objects are represented as light curves. Classifiers require training sets to learn the underlying patterns that allow the separation among classes. Unfortunately, building training sets is an expensive process that demands a lot of human efforts. Every time data comes from new surveys; the only available training instances are the ones that have a cross-match with previously labelled objects, consequently generating insufficient training sets compared with the large amounts of unlabelled sources. In this work, we present an algorithm that performs unsupervised classification of variable stars, relying only on the similarity among light curves. We tackle the unsupervised classification problem by proposing an untraditional approach. Instead of trying to match classes of stars with clusters found by a clustering algorithm, we propose a query based method where astronomers can find groups of variable stars ranked by similarity. We also develop a fast similarity function specific for light curves, based on a novel data structure that allows scaling the search over the entire dataset of unlabelled objects. Experiments show that our unsupervised model achieves high accuracy in the classification of different types of variable stars and that the proposed algorithm scales up to massive amounts of light curves.