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
  We report an automated morphological classification of galaxies into S-wise
spirals, Z-wise spirals, and non-spirals using big image data taken from
Subaru/Hyper Suprime-Cam (HSC) Survey and a convolutional neural
network(CNN)-based deep learning technique. The HSC i-band images are about 25
times deeper than those from the Sloan Digital Sky Survey (SDSS) and have a two
times higher spatial resolution, allowing us to identify substructures such as
spiral arms and bars in galaxies at z>0.1. We train CNN classifiers by using
HSC images of 1447 S-spirals, 1382 Z-spirals, and 51,650 non-spirals. As the
number of images in each class is unbalanced, we augment the data of spiral
galaxies by horizontal flipping, rotation, and rescaling of images to make the
numbers of three classes similar. The trained CNN models correctly classify
97.5% of the validation data, which is not used for training. We apply the CNNs
to HSC images of a half million galaxies with an i-band magnitude of i<20 over
an 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. Among
a total of 76,635 spiral galaxies, 48,576 are located at z>0.2, where we are
hardly able to identify spiral arms in the SDSS images. Our attempt
demonstrates that a combination of the HSC big data and CNNs has a large
potential to classify various types of morphology such as bars, mergers and
strongly-lensed objects.
