Automated detection of galaxy-scale gravitational lenses in high resolution imaging data

TL;DR

This paper presents an automated system that models and classifies galaxy-scale gravitational lenses in high-resolution images, achieving high purity and efficiency, to facilitate large-scale lens detection in future surveys.

Contribution

The authors develop a lens-modeling robot that automates the detection and classification of gravitational lenses, improving efficiency and reproducibility over manual methods.

Findings

Achieves ~100% purity with ~20% completeness using a simple model.

Can classify lens candidates at a rate of a few seconds per system.

Potential to process 10^7 galaxies in a large survey efficiently.

Abstract

Lens modeling is 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. Using a simple model optimized for "typical" galaxy-scale lenses, we generate four assessments of model quality that are used in an automated classification. The robot infers 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, including realistic simulated lenses and known false positives drawn from the HST/EGS survey. We compute the expected purity, completeness and rejection rate, and find that these can be optimized for a particular application by changing the prior probability distribution for H, equivalent to defining the robot's "character." Adopting a realistic prior based on the known 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 over-simplicity of the lens model. 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. We are able to classify lens candidates by eye at a rate of a few seconds per system, suggesting that a future 1000 square degree imaging survey containing 10^7 BRGs, and some 10^4 lenses, could be successfully, and reproducibly, searched in a modest amount of time. [Abridged]