2009ApJ...694..924M

We expect direct lens modeling to be the key to successful and meaningful automated strong galaxy-scale gravitational lens detection. We have implemented a lens-modeling "robot" that treats every bright red galaxy (BRG) in a large imaging survey as a potential gravitational lens system. Having optimized a simple model for "typical" galaxy-scale gravitational lenses, we generate four assessments of model quality that are then used in an automated classification. The robot infers from these four data the lens classification parameter H that a human would have assigned; the inference is performed using a probability distribution generated from a human-classified training set of candidates, including realistic simulated lenses and known false positives drawn from the Hubble Space Telescope (HST) Extended Groth Strip (EGS) survey. We compute the expected purity, completeness, and rejection rate, and find that these statistics can be optimized for a particular application by changing the prior probability distribution for H; this is equivalent to defining the robot's "character." Adopting a realistic prior based on expectations for the abundance of lenses, we find that a lens sample may be generated that is ∼100% pure, but only ∼20% complete. This shortfall is due primarily to the oversimplicity of the model of both the lens light and mass. With a more optimistic robot, ∼90% completeness can be achieved while rejecting ∼90% of the candidate objects. The remaining candidates must be classified by human inspectors. Displaying the images used and produced by the robot on a custom "one-click" web interface, we are able to inspect and classify lens candidates at a rate of a few seconds per system, suggesting that a future 1000 deg² imaging survey containing 10⁷ BRGs, and some 10⁴ lenses, could be successfully, and reproducibly, searched in a modest amount of time. We have verified our projected survey statistics, albeit at low significance, using the HST EGS data, discovering four new lens candidates in the process.