K2 Variable Catalogue II: Machine Learning Classification of Variable Stars and Eclipsing Binaries in K2 Fields 0-4

TL;DR

This paper introduces a novel machine learning approach combining Kohonen Self Organising Maps and Random Forests to classify variable stars and eclipsing binaries in K2 survey data, significantly expanding the catalog of known variables.

Contribution

It presents a new methodology for variable star classification using combined unsupervised and supervised machine learning techniques applied to K2 data, with open source code and a comprehensive catalog.

Findings

Identified 154 RR Lyrae stars, including 10 new discoveries

Classified thousands of variable stars and eclipsing binaries in K2 fields 0-4

Provided lightcurve features for stellar rotation and variability analysis

Abstract

We are entering an era of unprecedented quantities of data from current and planned survey telescopes. To maximise the potential of such surveys, automated data analysis techniques are required. Here we implement a new methodology for variable star classification, through the combination of Kohonen Self Organising Maps (SOM, an unsupervised machine learning algorithm) and the more common Random Forest (RF) supervised machine learning technique. We apply this method to data from the K2 mission fields 0-4, finding 154 ab-type RR Lyraes (10 newly discovered), 377 Delta Scuti pulsators, 133 Gamma Doradus pulsators, 183 detached eclipsing binaries, 290 semi-detached or contact eclipsing binaries and 9399 other periodic (mostly spot-modulated) sources, once class significance cuts are taken into account. We present lightcurve features for all K2 stellar targets, including their three strongest detected frequencies, which can be used to study stellar rotation periods where the observed variability arises from spot modulation. The resulting catalogue of variable stars, classes, and associated data features are made available online. We publish our SOM code in Python as part of the open source PyMVPA package, which in combination with already available RF modules can be easily used to recreate the method.