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

Abstract

We report an automated morphological classification of galaxies into S-wise spirals, Zwise 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.

Subaru Hyper Suprime-Cam excavates colossal over- and under-dense structures over 360 deg2 out to z=1

“…a red-sequence cluster search at 𝑧 = 0.1 -1.1 by Oguri et al 2018; a protocluster search at 𝑧 ∼ 4 by Toshikawa et al 2018). The high quality and large-volume data-set taken from the 8-meter class telescope permits statistical research of galaxy properties such as mass assembly histories and red fractions of galaxies in galaxy clusters (Lin et al 2017;Nishizawa et al 2018) and a morphological classification (Tadaki et al 2020) for more than ten thousands or millions of sources.…”