In the new era of large-scale astronomical surveys, automated methods of
analysis and classification of bulk data are a fundamental tool for fast and
efficient production of deliverables. This becomes ever more imminent as we
enter the Gaia era. We investigate the potential detectability of eclipsing
binaries with Gaia using a data set of all Kepler eclipsing binaries sampled
with Gaia cadence and folded with the Kepler period. The performance of fitting
methods is evaluated with comparison to real Kepler data parameters and a
classification scheme is proposed for the potentially detectable sources based
on the geometry of the light curve fits. The polynomial chain (polyfit) and
two-Gaussian models are used for light curve fitting of the data set.
Classification is performed with a combination of the t-SNE (t-distrubuted
Stochastic Neighbor Embedding) and DBSCAN (Density-Based Spatial Clustering of
Applications with Noise) algorithms. We find that approximately 68% of Kepler
Eclipsing Binary sources are potentially detectable by Gaia when folded with
the Kepler period and propose a classification scheme of the detectable sources
based on the morphological type indicative of the light curve, with subclasses
that reflect the properties of the fitted model (presence and visibility of
eclipses, their width, depth, etc.).Comment: 9 pages, 18 figures, accepted for publication in Astronomy &
Astrophysic
