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, 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.

12 pages, 10 figures, 3 tables, accepted for publication in MNRAS

References in corpus (12)

• Rotation-invariant convolutional neural networks for galaxy morphology prediction
• Galaxy Zoo 1 : Data Release of Morphological Classifications for nearly 900,000 galaxies
• The Evolution of Galaxy Structure over Cosmic Time
• Dawes Review 4: Spiral Structures in Disc Galaxies
• ZFOURGE/CANDELS: On the Evolution of M* Galaxy Progenitors from z=3 to 0.5
• Galaxy Zoo: The large-scale spin statistics of spiral galaxies in the Sloan Digital Sky Survey
• An automatic taxonomy of galaxy morphology using unsupervised machine learning
• Galaxy morphological classification in deep-wide surveys via unsupervised machine learning
• Estimating Luminosities and Stellar Masses of Galaxies Photometrically without Determining Redshifts
• Galaxy Zoo and SpArcFiRe: Constraints on spiral arm formation mechanisms from spiral arm number and pitch angles
• On the nature and correction of the spurious S-wise spiral galaxy winding bias in Galaxy Zoo 1
• Spin Parity of Spiral Galaxies I -- Corroborative Evidence for Trailing Spirals

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• SILVERRUSH X: Machine Learning-Aided Selection of $9318$ LAEs at $z=2.2$, $3.3$, $4.9$, $5.7$, $6.6$, and $7.0$ from the HSC SSP and CHORUS Survey Data
• Spin Parity of Spiral Galaxies III -- Dipole Analysis of the Distribution of SDSS Spirals with 3D Random Walk Simulations
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• HOLISMOKES -- XI. Evaluation of supervised neural networks for strong-lens searches in ground-based imaging surveys
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• HOLISMOKES XIII: Strong-lens candidates at all mass scales and their environments from the Hyper-Suprime Cam and deep learning
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• Galaxy Spin Classification I: Z-wise vs S-wise Spirals With Chirality Equivariant Residual Network
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