Improved methodology for the automated classification of periodic variable stars

Blomme, J. and Sarro, L. M. and O'Donovan, F. T. and Debosscher, J. and Brown, T. and Lopez, M. and Dubath, P. and Rimoldini, L. and Charbonneau, D. and Dunham, E. and Mandushev, G. and Ciardi, D. R. and De Ridder, J. and Aerts, C. (2011) Improved methodology for the automated classification of periodic variable stars. Monthly Notices of the Royal Astronomical Society, 418 (1). pp. 96-106. ISSN 0035-8711

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Abstract

We 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 ∼26000 stars. We found many eclipsing binaries as well as classical non-radial pulsators, such as slowly pulsating B stars, γ Doradus, β Cephei and δ Scuti stars. Also a few classical radial pulsators were found.


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Item Type: Article (Commonwealth Reporting Category C)
Refereed: Yes
Item Status: Live Archive
Additional Information: Access to published version in accordance with the copyright policy of the publisher.
Faculty / Department / School: No Faculty
Date Deposited: 14 Jun 2017 06:47
Last Modified: 14 Jun 2017 06:47
Uncontrolled Keywords: Statistical methods; data analysis; Techniques: photometric;
Fields of Research : 02 Physical Sciences > 0201 Astronomical and Space Sciences > 020110 Stellar Astronomy and Planetary Systems
02 Physical Sciences > 0201 Astronomical and Space Sciences > 020102 Astronomical and Space Instrumentation
Socio-Economic Objective: E Expanding Knowledge > 97 Expanding Knowledge > 970102 Expanding Knowledge in the Physical Sciences
Identification Number or DOI: 10.1111/j.1365-2966.2011.19466.x
URI: http://eprints.usq.edu.au/id/eprint/32155

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