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Achieving maximum scientific results from the overwhelming volume ofastronomical data to be acquired over the next few decades will demand novel,fully automatic methods of data analysis. Artificial intelligence approacheshold great promise in contributing to this goal. Here we apply neural networklearning technology to the specific domain of eclipsing binary (EB) stars, ofwhich only some hundreds have been rigorously analyzed, but whose numbers willreach millions in a decade. Well-analyzed EBs are a prime source ofastrophysical information whose growth rate is at present limited by the needfor human interaction with each EB data-set, principally in determining astarting solution for subsequent rigorous analysis. We describe the artificialneural network (ANN) approach which is able to surmount this human bottleneckand permit EB-based astrophysical information to keep pace with future datarates. The ANN, following training on a sample of 33,235 model light curves,outputs a set of approximate model parameters (T2/T1, (R1+R2)/a, e sin(omega),e cos(omega), and sin i) for each input light curve data-set. The whole sampleis processed in just a few seconds on a single 2GHz CPU. The obtainedparameters can then be readily passed to sophisticated modeling engines. Wealso describe a novel method polyfit for pre-processing observational lightcurves before inputting their data to the ANN and present the results andanalysis of testing the approach on synthetic data and on real data includingfifty binaries from the Catalog and Atlas of Eclipsing Binaries (CALEB)database and 2580 light curves from OGLE survey data. [abridged]

Artificial Intelligence Approach to the Determination of Physical Properties of Eclipsing Binaries. I. The EBAI Project