  Strong gravitationally lensed quasars provide powerful means to study galaxy
evolution and cosmology. Current and upcoming imaging surveys will contain
thousands of new lensed quasars, augmenting the existing sample by at least two
orders of magnitude. To find such lens systems, we built a robot, CHITAH, that
hunts for lensed quasars by modeling the configuration of the multiple quasar
images. Specifically, given an image of an object that might be a lensed
quasar, CHITAH first disentangles the light from the supposed lens galaxy and
the light from the multiple quasar images based on color information. A simple
rule is designed to categorize the given object as a potential four-image
(quad) or two-image (double) lensed quasar system. The configuration of the
identified quasar images is subsequently modeled to classify whether the object
is a lensed quasar system. We test the performance of CHITAH using simulated
lens systems based on the Canada-France-Hawaii Telescope Legacy Survey. For
bright quads with large image separations (with Einstein radius $r_{\rm
ein}>1.1"$) simulated using Gaussian point-spread functions, a high
true-positive rate (TPR) of $\sim$90% and a low false-positive rate of
$\sim$$3\%$ show that this is a promising approach to search for new lens
systems. We obtain high TPR for lens systems with $r_{\rm ein}\gtrsim0.5"$, so
the performance of CHITAH is set by the seeing. We further feed a known
gravitational lens system, COSMOS 5921$+$0638, to CHITAH, and demonstrate that
CHITAH is able to classify this real gravitational lens system successfully.
Our newly built CHITAH is omnivorous and can hunt in any ground-based imaging
surveys.
