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We report an automated morphological classification of galaxies into S-wisespirals, Z-wise spirals, and non-spirals using big image data taken fromSubaru/Hyper Suprime-Cam (HSC) Survey and a convolutional neuralnetwork(CNN)-based deep learning technique. The HSC i-band images are about 25times deeper than those from the Sloan Digital Sky Survey (SDSS) and have a twotimes higher spatial resolution, allowing us to identify substructures such asspiral arms and bars in galaxies at z>0.1. We train CNN classifiers by usingHSC images of 1447 S-spirals, 1382 Z-spirals, and 51,650 non-spirals. As thenumber of images in each class is unbalanced, we augment the data of spiralgalaxies by horizontal flipping, rotation, and rescaling of images to make thenumbers of three classes similar. The trained CNN models correctly classify97.5% of the validation data, which is not used for training. We apply the CNNsto HSC images of a half million galaxies with an i-band magnitude of i<20 overan area of 320 deg^2. 37,917 S-spirals and 38,718 Z-spirals are identified,indicating no significant difference between the numbers of two classes. Amonga total of 76,635 spiral galaxies, 48,576 are located at z>0.2, where we arehardly able to identify spiral arms in the SDSS images. Our attemptdemonstrates that a combination of the HSC big data and CNNs has a largepotential to classify various types of morphology such as bars, mergers andstrongly-lensed objects.

Spin Parity of Spiral Galaxies II: A catalogue of 80k spiral galaxies using big data from the Subaru Hyper Suprime-Cam Survey and deep learning