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We apply a novel model based on convolutional neural networks (CNNs) toidentify gravitationally-lensed galaxies in multi-band imaging of the HyperSuprime Cam Subaru Strategic Program (HSC-SSP) Survey. The trained model isapplied to a parent sample of 2 350 061 galaxies selected from the $\sim$ 800deg$^2$ Wide area of the HSC-SSP Public Data Release 2. The galaxies in HSCWide are selected based on stringent pre-selection criteria, such as multibandmagnitudes, stellar mass, star formation rate, extendedness limit, photometricredshift range, etc. Initially, the CNNs provide a total of 20 241 cutouts witha score greater than 0.9, but this number is subsequently reduced to 1 522cutouts by removing definite non-lenses for further inspection by human eyes.We discover 43 definite and 269 probable lenses, of which 97 are completelynew. In addition, out of 880 potential lenses, we recovered 289 known systemsin the literature. We identify 143 candidates from the known systems that hadhigher confidence in previous searches. Our model can also recover 285candidate galaxy-scale lenses from the Survey of Gravitationally lensed Objectsin HSC Imaging (SuGOHI), where a single foreground galaxy acts as thedeflector. Even though group-scale and cluster-scale lens systems were notincluded in the training, a sample of 32 SuGOHI-c (i.e., group/cluster-scalesystems) lens candidates was retrieved. Our discoveries will be useful forongoing and planned spectroscopic surveys, such as the Subaru Prime FocusSpectrograph project, to measure lens and source redshifts in order to enabledetailed lens modelling.

Survey of Gravitationally lensed Objects in HSC Imaging (SuGOHI) $-$ X. Strong Lens Finding in The HSC-SSP using Convolutional Neural Networks