Speaker
Dóra Tarczay-Nehéz
(CSFK CSI)
Description
The most recent telescopes (e.g. Kepler, K2, Gaia, TESS) and sky surveys (e.g. SSDS, and the forthcoming LSST) provide huge amount of data, that leads to the challenge of data processing. This huge volume of data needs to be analyzed with fast and effective automated computer programming techniques. Therefore, machine learning algorithms become popular in astronomy, as they can play a key role in automatic classification of variable stars. In this work, we present our machine learning algorithm for searching variable stars, based on the statistical characteristics of light curves, that represent the brightness variability of the stars in the Gaia DR2 database.
Primary author
Dóra Tarczay-Nehéz
(CSFK CSI)
Co-authors
Dr
Róbert Szabó
(CSFK CSI)
Zoltán Szeleczky
(University of Liverpool)
