Context : Strong lenses are extremely useful probes of the distribution of matter on galaxy and cluster scales at cosmological distances , but are rare and difficult to find .
The number of currently known lenses is on the order of 1,000 .

Aims : We wish to use crowdsourcing to carry out a lens search targeting massive galaxies selected from over 442 square degrees of photometric data from the Hyper Suprime-Cam ( HSC ) survey .

Methods : We selected from the S16A internal data release of the HSC survey a sample of \sim 300 , 000 galaxies with photometric redshifts in the range 0.2 < z _ { \mathrm { phot } } < 1.2 and photometrically inferred stellar masses \log { M _ { * } } > 11.2 .
We crowdsourced lens finding on this sample of galaxies on the Zooniverse platform , as part of the Space Warps project .
The sample was complemented by a large set of simulated lenses and visually selected non-lenses , for training purposes .
Nearly 6 , 000 citizen volunteers participated in the experiment .
In parallel , we used YattaLens an automated lens finding algorithm , to look for lenses in the same sample of galaxies .

Results : Based on a statistical analysis of classification data from the volunteers , we selected a sample of the most promising \sim 1 , 500 candidates which we then visually inspected : half of them turned out to be possible ( grade C ) lenses or better .
Including lenses found by YattaLens or serendipitously noticed in the discussion section of the Space Warps website , we were able to find 14 definite lenses ( grade A ) , 129 probable lenses ( grade B ) and 581 possible lenses . YattaLens found half the number of lenses discovered via crowdsourcing .

Conclusions : Crowdsourcing is able to produce samples of lens candidates with high completeness and purity , compared to currently available automated algorithms .
A hybrid approach , in which the visual inspection of samples of lens candidates pre-selected by discovery algorithms and/or coupled to machine learning is crowdsourced , will be a viable option for lens finding in the 2020s with forthcoming wide area surveys such as LSST , Euclid and WFIRST .