Improved methodology for the automated classification of periodic variable stars
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 ∼26 000 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.
- Publication:
-
Monthly Notices of the Royal Astronomical Society
- Pub Date:
- November 2011
- DOI:
- 10.1111/j.1365-2966.2011.19466.x
- arXiv:
- arXiv:1101.5038
- Bibcode:
- 2011MNRAS.418...96B
- Keywords:
-
- methods: data analysis;
- methods: statistical;
- techniques: photometric;
- Astrophysics - Instrumentation and Methods for Astrophysics
- E-Print:
- 11 pages, 6 figures Accepted for publication in MNRAS