Improved methodology for the automated classification of periodic variable stars
Sarro, L. M.
O’Donovan, F. T.
Ciardi, D. R.
De Ridder, J.
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CitationBlomme, J., L. M. Sarro, F. T. O’Donovan, J. Debosscher, T. Brown, M. Lopez, P. Dubath, et al. 2011. “Improved Methodology for the Automated Classification of Periodic Variable Stars.” Monthly Notices of the Royal Astronomical Society 418 (1): 96–106. https://doi.org/10.1111/j.1365-2966.2011.19466.x.
AbstractWe present a novel automated methodology to detect and classify periodic variable stars in a large data base of photometric time series. The methods are based on multivariate Bayesian statistics and use a multistage approach. We applied our method to the ground-based data of the Trans-Atlantic Exoplanet Survey (TrES) Lyr1 field, which is also observed by the Kepler satellite, covering similar to 26 000 stars. We found many eclipsing binaries as well as classical non-radial pulsators, such as slowly pulsating B stars, ? Doradus, beta Cephei and d Scuti stars. Also a few classical radial pulsators were found.
Citable link to this pagehttp://nrs.harvard.edu/urn-3:HUL.InstRepos:41417397
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