# K2 variable catalogue – II. Machine learning classification of variable stars and eclipsing binaries in K2 fields 0–4

@article{Armstrong2016K2VC,
title={K2 variable catalogue – II. Machine learning classification of variable stars and eclipsing binaries in K2 fields 0–4},
author={David J Armstrong and Jason M. Kirk and Kristine W. F. Lam and J. McCormac and Hugh Osborn and Jessica J Spake and Simon. R. Walker and D. J. A. Brown and Martti H. Kristiansen and Don Pollacco and Richard G. West and Peter J. Wheatley},
journal={Monthly Notices of the Royal Astronomical Society},
year={2016},
volume={456},
pages={2260-2272}
}
• Published 3 December 2015
• Physics
• Monthly Notices of the Royal Astronomical Society
We are entering an era of unprecedented quantities of data from current and planned survey telescopes. To maximize 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-Organizing Maps (SOMs, 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…

## Figures and Tables from this paper

A machine learned classifier for RR Lyrae in the VVV survey
• Computer Science
• 2016
This work describes a supervised machine-learned classifier constructed for assigning a score to a K s -band VVV light curve that indicates its likelihood of being ab -type RR Lyrae, and obtains a classifier based on the AdaBoost algorithm that achieves a harmonic mean between false positives and false negatives of ≈7% for typical VvV light-curve sets.
Discovery of 36 eclipsing EL CVn binaries found by the Palomar Transient Factory
• Physics
• 2017
We report on the discovery and analysis of 36 new eclipsing EL CVn-type binaries, consisting of a core helium-composition pre-white dwarf (pre-He-WD) and an early-type main-sequence companion. This
A detection metric designed for O’Connell effect eclipsing binaries
• Physics
Computational Astrophysics and Cosmology
• 2019
We present the construction of a novel time-domain signature extraction methodology and the development of a supporting supervised pattern detection algorithm. We focus on the targeted identification
Eclipsing Binaries in the MOA-II Database
The results suggest that contact binaries with periods close to the 0.22-day contact binary limit are commonly accompanied by relatively close tertiary companions.
How to Find Variable Active Galactic Nuclei with Machine Learning
• Physics, Computer Science
The Astrophysical Journal
• 2019
This work focuses on an unsupervised method based on self-organizing maps (SOM) that is applied to a set of nonparametric variability estimators that can be applied to any time-sampled light curve.
Classification of OGLE Eclipsing Binary Stars Based on Their Morphology Type with Locally Linear Embedding
• Physics, Computer Science
• 2021
This work uses a dimensionality reduction technique with locally linear embedding to map the high dimension of the data set into a low-dimensional embedding parameter space, while keeping the local geometry and the similarities of the neighboring data points.
Discovery of new dipper stars with K2: a window into the inner disc region of T Tauri stars
• Physics, Geology
• 2018
In recent years, a new class of young stellar object (YSO) has been defined, referred to as dippers, where large transient drops in flux are observed. These dips are too large to be attributed to
Identifying Periodic Variable Stars and Eclipsing Binary Systems with Long-term Las Cumbres Observatory Photometric Monitoring of ZTF J0139+5245
• Physics
The Astronomical Journal
• 2021
We present the results of our search for variable stars using the long-term Las Cumbres Observatory (LCO) monitoring of white dwarf ZTF J0139+5245 with the two 1.0 m telescope nodes located at
Advanced Astroinformatics for Variable Star Classification
It is proposed that a complete machine learning strategy for use in the upcoming era of big data from the next generation of big telescopes, such as LSST, must consider this type of design integration.
The ASAS-SN catalogue of variable stars I: The Serendipitous Survey
• Physics
• 2018
The All-Sky Automated Survey for Supernovae (ASAS-SN) is the first optical survey to routinely monitor the whole sky with a cadence of $\sim2-3$ days down to V$\lesssim17$ mag. ASAS-SN has monitored

## References

SHOWING 1-10 OF 57 REFERENCES
K2 Variable Catalogue: Variable Stars and Eclipsing Binaries in K2 Campaigns 1 and 0
• Physics
• 2015
We have created a catalogue of variable stars found from a search of the publicly available K2 mission data from Campaigns 1 and 0. This catalogue provides the identifiers of 8395 variable stars,
Automated classification of variable stars for All‐Sky Automated Survey 1–2 data
• Physics
• 2001
With the advent of surveys generating multi-epoch photometry and the discovery of large numbers of variable stars, the classification of these stars has to be automatic. We have developed such a
AUTOMATED CLASSIFICATION OF PERIODIC VARIABLE STARS DETECTED BY THE WIDE-FIELD INFRARED SURVEY EXPLORER
• Computer Science
• 2014
It is described a methodology to classify periodic variable stars identified using photometric time-series measurements constructed from the Wide-field Infrared Survey Explorer (WISE) full-mission single-exposure Source Databases and shows that Fourier decomposition techniques can be extended into the mid-IR to assist with their classification.
Kepler Eclipsing Binary Stars. VI. Identification of Eclipsing Binaries in the K2 Campaign 0 Data-set
• Physics, Geology
• 2015
The original {\it Kepler} mission observed and characterized over 2400 eclipsing binaries in addition to its prolific exoplanet detections. Despite the mechanical malfunction and subsequent
Supervised detection of anomalous light-curves in massive astronomical catalogs
• Computer Science
ArXiv
• 2014
A new method to automatically discover unknown variable objects in large astronomical catalogs is presented, based on a supervised algorithm that trains a random forest classifier using known variability classes of objects and obtains votes for each of the objects in the training set.
ON MACHINE-LEARNED CLASSIFICATION OF VARIABLE STARS WITH SPARSE AND NOISY TIME-SERIES DATA
• Computer Science
• 2011
A methodology for variable-star classification, drawing from modern machine-learning techniques, which is effective for identifying samples of specific science classes and presents the first astronomical use of hierarchical classification methods to incorporate a known class taxonomy in the classifier.
Automated Classification of Variable Stars in the Asteroseismology Program of the Kepler Space Mission
• Physics, Geology
• 2010
We present the first results of the application of supervised classification methods to the Kepler Q1 long-cadence light curves of a subsample of 2288 stars measured in the asteroseismology program
KEPLER ECLIPSING BINARY STARS. I. CATALOG AND PRINCIPAL CHARACTERIZATION OF 1879 ECLIPSING BINARIES IN THE FIRST DATA RELEASE
• Physics, Geology
• 2010
The Kepler space mission is devoted to finding Earth-size planets orbiting other stars in their habitable zones. Its large, 105 deg2 field of view features over 156,000 stars that are observed
Detection of a large sample of γ Doradus stars from Kepler space photometry and high-resolution ground-based spectroscopy
• Physics
• 2013
The space-missions MOST, CoRoT, and Kepler deliver a huge amount of high-quality photometric data suitable to study numerous pulsating stars. Our ultimate goal is a detection and analysis of an
Global stellar variability study in the field-of-view of the Kepler satellite
• Physics
• 2011
Aims. We present the results of an automated variability analysis of the Kepler public data measured in the first quarter (Q1) of the mission. In total, about 150000 light curves have been analysed