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K2 Variable Catalogue II: Machine Learning Classification of Variable Stars and Eclipsing Binaries in K2 Fields 0-4
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.Comment: Accepted for publication in MNRAS, 16 pages, 13 figures. Updated with
proof corrections. Full catalogue tables available at
https://www2.warwick.ac.uk/fac/sci/physics/research/astro/people/armstrong/
or at the CD
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