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Title: EXPLORING THE VARIABLE SKY WITH LINEAR. III. CLASSIFICATION OF PERIODIC LIGHT CURVES

Journal Article · · Astronomical Journal (New York, N.Y. Online)
; ;  [1]; ; ; ; ; ;  [2]; ; ;  [3];  [4]; ; ; ;  [5];  [6];  [7];  [8] more »; « less
  1. Observatoire Astronomique de l'Université de Genève, 51 chemin des Maillettes, CH-1290 Sauverny (Switzerland)
  2. Department of Astronomy, University of Washington, P.O. Box 351580, Seattle, WA 98195-1580 (United States)
  3. Hvar Observatory, Faculty of Geodesy, Kačićeva 26, 10000 Zagreb (Croatia)
  4. Faculty of Geodesy, Kačićeva 26, 10000 Zagreb (Croatia)
  5. Department of Physics, Faculty of Science, University of Zagreb, Bijenička cesta 32, 10000 Zagreb (Croatia)
  6. Division of Physics, Mathematics, and Astronomy, Caltech, Pasadena, CA 91125 (United States)
  7. Lincoln Laboratory, Massachusetts Institute of Technology, 244 Wood Street, Lexington, MA 02420-9108 (United States)
  8. Saršoni 90, 51216 Viškovo (Croatia)

We describe the construction of a highly reliable sample of ∼7000 optically faint periodic variable stars with light curves obtained by the asteroid survey LINEAR across 10,000 deg{sup 2} of the northern sky. The majority of these variables have not been cataloged yet. The sample flux limit is several magnitudes fainter than most other wide-angle surveys; the photometric errors range from ∼0.03 mag at r = 15 to ∼0.20 mag at r = 18. Light curves include on average 250 data points, collected over about a decade. Using Sloan Digital Sky Survey (SDSS) based photometric recalibration of the LINEAR data for about 25 million objects, we selected ∼200,000 most probable candidate variables with r < 17 and visually confirmed and classified ∼7000 periodic variables using phased light curves. The reliability and uniformity of visual classification across eight human classifiers was calibrated and tested using a catalog of variable stars from the SDSS Stripe 82 region and verified using an unsupervised machine learning approach. The resulting sample of periodic LINEAR variables is dominated by 3900 RR Lyrae stars and 2700 eclipsing binary stars of all subtypes and includes small fractions of relatively rare populations such as asymptotic giant branch stars and SX Phoenicis stars. We discuss the distribution of these mostly uncataloged variables in various diagrams constructed with optical-to-infrared SDSS, Two Micron All Sky Survey, and Wide-field Infrared Survey Explorer photometry, and with LINEAR light-curve features. We find that the combination of light-curve features and colors enables classification schemes much more powerful than when colors or light curves are each used separately. An interesting side result is a robust and precise quantitative description of a strong correlation between the light-curve period and color/spectral type for close and contact eclipsing binary stars (β Lyrae and W UMa): as the color-based spectral type varies from K4 to F5, the median period increases from 5.9 hr to 8.8 hr. These large samples of robustly classified variable stars will enable detailed statistical studies of the Galactic structure and physics of binary and other stars and we make these samples publicly available.

OSTI ID:
22273333
Journal Information:
Astronomical Journal (New York, N.Y. Online), Vol. 146, Issue 4; Other Information: Country of input: International Atomic Energy Agency (IAEA); ISSN 1538-3881
Country of Publication:
United States
Language:
English