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 1000. 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. We selected from the S16A internal data release of the HSC survey a sample of ~300000 galaxies with photometric redshifts in the range 0.2<zphot11.2. 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 6000 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. Based on a statistical analysis of classification data from the volunteers, we selected a sample of the most promising ~1500 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. 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.

Cone search capability for table J/A+A/642/A148/sugohi (SuGOHI catalog)