  We have developed a fully-automated pipeline for systematically identifying
and analyzing eclipsing binaries within large datasets of light curves. The
pipeline is made up of multiple tiers which subject the light curves to
increasing levels of scrutiny. After each tier, light curves that did not
conform to a given criteria were filtered out of the pipeline, reducing the
load on the following, more computationally intensive tiers. As a central
component of the pipeline, we created the fully automated Detached Eclipsing
Binary Light curve fitter (DEBiL), which rapidly fits large numbers of light
curves to a simple model. Using the results of DEBiL, light curves of interest
can be flagged for follow-up analysis. As a test case, we analyzed the 218699
light curves within the bulge fields of the OGLE II survey and produced 10862
model fits. We point out a small number of extreme examples as well as
unexpected structure found in several of the population distributions. We
expect this approach to become increasingly important as light curve datasets
continue growing in both size and number.
