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The advent of large scale multi-epoch surveys raises the need for automatedlight curve (LC) processing. This is particularly true for eclipsing binaries(EBs), which form one of the most populated types of variable objects. The Gaiamission, launched at the end of 2013, is expected to detect of the order of fewmillion EBs over a 5-year mission.  We present an automated procedure to characterize EBs based on the geometricmorphology of their LCs with two aims: first to study an ensemble of EBs on astatistical ground without the need to model the binary system, and second toenable the automated identification of EBs that display atypical LCs. We modelthe folded LC geometry of EBs using up to two Gaussian functions for theeclipses and a cosine function for any ellipsoidal-like variability that may bepresent between the eclipses. The procedure is applied to the OGLE-III data setof EBs in the Large Magellanic Cloud (LMC) as a proof of concept. The bayesianinformation criterion is used to select the best model among models containingvarious combinations of those components, as well as to estimate thesignificance of the components.  Based on the two-Gaussian models, EBs with atypical LC geometries aresuccessfully identified in two diagrams, using the Abbe values of the originaland residual folded LCs, and the reduced $\chi^2$. Cleaning the data set fromthe atypical cases and further filtering out LCs that contain non-significanteclipse candidates, the ensemble of EBs can be studied on a statistical groundusing the two-Gaussian model parameters. For illustration purposes, we presentthe distribution of projected eccentricities as a function of orbital periodfor the OGLE-III set of EBs in the LMC, as well as the distribution of theirprimary versus secondary eclipse widths.

Gaia eclipsing binary and multiple systems. Two-Gaussian models applied to OGLE-III eclipsing binary light curves in the Large Magellanic Cloud