Search

We report the discovery of 29 promising (and 59 total) new lens candidates from the Canada–France–Hawaii Telescope 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. Space Warps recovers about 65 per cent 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 per cent 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.

We develop a method to enable collaborative modelling of gravitational lenses and lens candidates, that could be used by non-professional lens enthusiasts. It uses an existing free-form modelling program (glass), but enables the input to this code to be provided in a novel way, via a user-generated diagram that is essentially a sketch of an arrival-time surface. We report on an implementation of this method, SpaghettiLens, which has been tested in a modelling challenge using 29 simulated lenses drawn from a larger set created for the Space Warps citizen science strong lens search. We find that volunteers from this online community asserted the image parities and time ordering consistently in some lenses, but made errors in other lenses depending on the image morphology. While errors in image parity and time ordering lead to large errors in the mass distribution, the enclosed mass was found to be more robust: the model-derived Einstein radii found by the volunteers were consistent with those produced by one of the professional team, suggesting that given the appropriate tools, gravitational lens modelling is a data analysis activity that can be crowd-sourced to good effect. Ideas for improvement are discussed, these include (a) overcoming the tendency of the models to be shallower than the correct answer in test cases, leading to systematic overestimation of the Einstein radius by 10 per cent at present, and (b) detailed modelling of arcs.