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 _ { ein } > 1 \farcs 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 _ { ein } \gtrsim 0.5 ^ { \prime \prime } , 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 .