HSC-SSP lens candidates from neural networks

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 preselections 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.