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 magnitud
e. 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.
A new approximation method for inverting the Poissons equation is presented for a continuously distributed and finite-sized source in an unbound domain. The advantage of this image multipole method arises from its ability to place the computational e
rror close to the computational domain boundary, making the source region almost error free. It is contrasted to the modified Greens function method that has small but finite errors in the source region. Moreover, this approximation method also has a systematic way to greatly reduce the errors at the expense of somewhat greater computational efforts. Numerical examples of three-dimensional and two-dimensional cases are given to illustrate the advantage of the new method.
