A Computer Vision Approach To Identify Einstein Rings And Arcs
  Einstein rings are rare gem of the strong lensing phenomena. Unlike doubly or
quadruply lensed systems, the ring images can be used to probe the underlying
lens gravitational potential at every position angle, putting much tighter
constraints on the lens mass profile. In addition, the magnified background
source also enable us to probe high-z galaxies with enhanced spatial resolution
and higher S/N, which is otherwise not possible for un-lensed galaxy studies.
Despite their usefulness, only a handful of Einstein rings have been reported
so far, mainly by serendipitous discoveries or visual inspections of hundred
thousands of massive galaxies or galaxy clusters. With the on-going and
forth-coming large area surveys such as Large Synoptic Survey Telescope, visual
inspection to discover Einstein rings is very difficult, and an automated
approach to identify ring pattern in the big data to come is in high demand.
Here we present an Einstein ring recognition approach based on computer vision
techniques. The workhorse is the circle Hough transform, which can recognize
circular patterns or arcs at any position with any radius in the images. We
devise a two-tier approach: first pre-select LRGs associated with multiple blue
objects as possible lens galaxies, then feed these possible lenses to Hough
transform. As a proof-of-concept, we investigate our approach using the Sloan
Digital Sky Surveys. Our results show high completeness, albeit low purity. We
also apply our approach to three newly discovered Einstein rings/arcs, in the
DES, HSC-SSP, and UltraVISTA survey, illustrating the versatility of our
approach to on-going and up-coming large sky surveys in general. The beauty of
our approach is that it is solely based on JPEG images, which can be easily
obtained in batch mode from SDSS finding chart tools, without any
pre-processing of the image. (Abridged)
