Abstract
Large surveys producing tera- and petabyte-scale databases require
machine-learning and knowledge discovery methods to deal with the overwhelming
quantity of data and the difficulties of extracting concise, meaningful
information with reliable assessment of its uncertainty. This study
investigates the potential of a few machine-learning methods for the automated
analysis of eclipsing binaries in the data of such surveys. We aim to aid the
extraction of samples of eclipsing binaries from such databases and to provide
basic information about the objects. We estimate class labels according to two
classification systems, one based on the light curve morphology (EA/EB/EW
classes) and the other based on the physical characteristics of the binary
system (system morphology classes; detached through overcontact systems).
Furthermore, we explore low-dimensional surfaces along which the light curves
of eclipsing binaries are concentrated, to use in the characterization of the
binary systems and in the exploration of biases of the full unknown Gaia data
with respect to the training sets. We explore the performance of principal
component analysis (PCA), linear discriminant analysis (LDA), random forest
classification and self-organizing maps (SOM). We pre-process the photometric
time series by combining a double Gaussian profile fit and a smoothing spline,
in order to de-noise and interpolate the observed light curves. We achieve
further denoising, and selected the most important variability elements from
the light curves using PCA. We perform supervised classification using random
forest and LDA based on the PC decomposition, while SOM gives a continuous
2-dimensional manifold of the light curves arranged by a few important
features. We estimate the uncertainty of the supervised methods due to the
specific finite training set using ensembles of models constructed on
randomized training sets.
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