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}
}
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… 
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References

SHOWING 1-10 OF 57 REFERENCES
K2 Variable Catalogue: Variable Stars and Eclipsing Binaries in K2 Campaigns 1 and 0
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
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
TLDR
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
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
TLDR
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
TLDR
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
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
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
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
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
...
1
2
3
4
5
...