Space Warps II. New Gravitational Lens Candidates from the CFHTLS Discovered through Citizen Science

TL;DR

This paper reports the discovery of 29 new gravitational lens candidates from the CFHTLS through citizen science, demonstrating the effectiveness of human classifiers in complementing automated lens detection methods.

Contribution

Introduces the Space Warps citizen science approach, compares its results with automated algorithms, and provides a new pipeline and training set for future lens detection.

Findings

Space Warps recovered about 65% of known lenses.

29 new promising lens candidates identified.

Training pipeline and dataset made publicly available.

Abstract

We report the discovery of 29 promising (and 59 total) new lens candidates from the CFHT Legacy Survey (CFHTLS) based on about 11 million classifications performed by citizen scientists as part of the first Space Warps lens search. The goal of the blind lens search was to identify lens candidates missed by robots (the RingFinder on galaxy scales and ArcFinder on group/cluster scales) which had been previously used to mine the CFHTLS for lenses. We compare some properties of the samples detected by these algorithms to the Space Warps sample and find them to be broadly similar. The image separation distribution calculated from the Space Warps sample shows that previous constraints on the average density profile of lens galaxies are robust. SpaceWarps recovers about 65% of known lenses, while the new candidates show a richer variety compared to those found by the two robots. This detection rate could be increased to 80% by only using classifications performed by expert volunteers (albeit at the cost of a lower purity), indicating that the training and performance calibration of the citizen scientists is very important for the success of Space Warps. In this work we present the SIMCT pipeline, used for generating in situ a sample of realistic simulated lensed images. This training sample, along with the false positives identified during the search, has a legacy value for testing future lens finding algorithms. We make the pipeline and the training set publicly available.