  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.
