  We have carried out a systematic search for galaxy-scale strong lenses in
multiband imaging from the Hyper Suprime-Cam (HSC) survey. Our automated
pipeline, based on realistic strong-lens simulations, deep neural network
classification, and visual inspection, is aimed at efficiently selecting
systems with wide image separations (Einstein radii ~1.0-3.0"), intermediate
redshift lenses (z ~ 0.4-0.7), and bright arcs for galaxy evolution and
cosmology. We classified gri images of all 62.5 million galaxies in HSC Wide
with i-band Kron radius >0.8" to avoid strict pre-selections and to prepare for
the upcoming era of deep, wide-scale imaging surveys with Euclid and Rubin
Observatory. We obtained 206 newly-discovered candidates classified as definite
or probable lenses with either spatially-resolved multiple images or extended,
distorted arcs. In addition, we found 88 high-quality candidates that were
assigned lower confidence in previous HSC searches, and we recovered 173 known
systems in the literature. These results demonstrate that, aided by limited
human input, deep learning pipelines with false positive rates as low as ~0.01%
can be very powerful tools for identifying the rare strong lenses from large
catalogs, and can also largely extend the samples found by traditional
algorithms. We provide a ranked list of candidates for future spectroscopic
confirmation.
