Automatic morphological classification of galaxies: convolutional autoencoder and bagging-based multiclustering model

Abstract

In order to obtain morphological information of unlabeled galaxies, we present an unsupervised machine-learning (UML) method for morphological classification of galaxies, which can be summarized as two aspects: (1) the methodology of convolutional autoencoder (CAE) is used to reduce the dimensions and extract features from the imaging data; (2) the bagging-based multiclustering model is proposed to obtain the classifications with high confidence at the cost of rejecting the disputed sources that are inconsistently voted. We apply this method on the sample of galaxies with $H<24.5$ in CANDELS. Galaxies are clustered into 100 groups, each contains galaxies with analogous characteristics. To explore the robustness of the morphological classifications, we merge 100 groups into five categories by visual verification, including spheroid, early-type disk, late-type disk, irregular, and unclassifiable. After eliminating the unclassifiable category and the sources with inconsistent voting, the purity of the remaining four subclasses are significantly improved. Massive galaxies ($M_{\ast }>10^{10}{M}_{\odot }$) are selected to investigate the connection with other physical properties. The classification scheme separates galaxies well in the U-V and V-J color space and Gini-$M_{20}$ space. The gradual tendency of Sérsic indexes and effective radii is shown from the spheroid subclass to the irregular subclass. It suggests that the combination of CAE and multi-clustering strategy is an effective method to cluster galaxies with similar features and can yield high-quality morphological classifications. Our study demonstrates the feasibility of UML in morphological analysis that would develop and serve the future observations made with China Space Station telescope.

arXiv comment17 pages, 13 figures, To be published in AJ

References in corpus (50)

• Stellar population synthesis at the resolution of 2003
• Galactic Stellar and Substellar Initial Mass Function
• The Dust Content and Opacity of Actively Star-Forming Galaxies
• Ward's Hierarchical Clustering Method: Clustering Criterion and Agglomerative Algorithm
• Detailed Structural Decomposition of Galaxy Images
• CANDELS: The Cosmic Assembly Near-infrared Deep Extragalactic Legacy Survey
• CANDELS: The Cosmic Assembly Near-infrared Deep Extragalactic Legacy Survey - The Hubble Space Telescope Observations, Imaging Data Products and Mosaics
• EAZY: A Fast, Public Photometric Redshift Code
• The Dependence of Star Formation History and Internal Structure on Stellar Mass for 10^5 Low-Redshift Galaxies
• 3D-HST+CANDELS: The Evolution of the Galaxy Size-Mass Distribution since $z=3$
• The Environmental Dependence of the Relations between Stellar Mass, Structure, Star Formation and Nuclear Activity in Galaxies
• An ultra-deep near-infrared spectrum of a compact quiescent galaxy at z=2.2
• 3D-HST WFC3-selected Photometric Catalogs in the Five CANDELS/3D-HST Fields: Photometry, Photometric Redshifts and Stellar Masses
• Detection of quiescent galaxies in a bicolor sequence from z=0-2
• The Relationship Between Stellar Light Distributions of Galaxies and their Formation Histories
• A New Non-Parametric Approach to Galaxy Morphological Classification
• Rotation-invariant convolutional neural networks for galaxy morphology prediction
• Galaxy Zoo 1 : Data Release of Morphological Classifications for nearly 900,000 galaxies
• The 3D-HST Survey: Hubble Space Telescope WFC3/G141 grism spectra, redshifts, and emission line measurements for $\sim 100,000$ galaxies
• The Green Valley is a Red Herring: Galaxy Zoo reveals two evolutionary pathways towards quenching of star formation in early- and late-type galaxies
• Structural Parameters of Galaxies in CANDELS
• The NEWFIRM Medium-band Survey: Photometric Catalogs, Redshifts and the Bimodal Color Distribution of Galaxies out to z~3
• The Evolution of Galaxy Structure over Cosmic Time
• Bulge Growth and Quenching since z = 2.5 in CANDELS/3D-HST
• The Millennium Galaxy Catalogue: morphological classification and bimodality in the colour-concentration plane
• A catalog of visual-like morphologies in the 5 CANDELS fields using deep-learning
• Bulge mass is king: The dominant role of the bulge in determining the fraction of passive galaxies in the Sloan Digital Sky Survey
• The FourStar Galaxy Evolution Survey (ZFOURGE): ultraviolet to far-infrared catalogs, medium-bandwidth photometric redshifts with improved accuracy, stellar masses, and confirmation of quiescent galaxies to z~3.5
• Effect of local environment and stellar mass on galaxy quenching and morphology at $0.5<z<2.0$
• CANDELS Visual Classifications: Scheme, Data Release, and First Results
• A robust morphological classification of high-redshift galaxies using support vector machines on seeing limited images. I Method description
• Revealing velocity dispersion as the best indicator of a galaxy's color, compared to stellar mass, surface mass density or morphology
• Two Conditions for Galaxy Quenching: Compact Centres and Massive Haloes
• The connection between galaxy structure and quenching efficiency
• AEGIS: The Morphologies of Green Galaxies at 0.4<z<1.2
• Demographics of Star-forming Galaxies since $z\sim 2.5$. I. The $UVJ$ Diagram in CANDELS
• Galaxy Colour, Morphology, and Environment in the Sloan Digital Sky Survey
• An automatic taxonomy of galaxy morphology using unsupervised machine learning
• New Image Statistics for Detecting Disturbed Galaxy Morphologies at High Redshift
• The Gemini Deep Deep Survey: VIII. When Did Early-type Galaxies Form?
• Identifying Strong Lenses with Unsupervised Machine Learning using Convolutional Autoencoder
• An Artificial Neural Network Approach For Ranking Quenching Parameters In Central Galaxies
• The Millennium Galaxy Catalogue: Exploring the Color-Concentration Bimodality via Bulge-Disc Decomposition
• Identification of Low Surface Brightness Tidal Features in Galaxies Using Convolutional Neural Networks
• Beyond the Hubble Sequence -- Exploring Galaxy Morphology with Unsupervised Machine Learning
• The Relationship Between Star-formation Activity and Galaxy Structural Properties in CANDELS and a Semi-analytic Model
• Cataloging the radio-sky with unsupervised machine learning: a new approach for the SKA era
• Radio Galaxy Zoo: Unsupervised Clustering of Convolutionally Auto-encoded Radio-astronomical Images
• The morphological evolution, AGN fractions, dust content, environments, and downsizing of massive green valley galaxies at 0.5<z<2.5 in 3D-HST/CANDELS
• Dust properties and star formation of approximately a thousand local galaxies

Cited by in corpus (2)

• Automatic Classification of Galaxy Morphology: a rotationally invariant supervised machine learning method based on the UML-dataset
• The Three Hundred project: A Machine Learning method to infer clusters of galaxies mass radial profiles from mock Sunyaev-Zel'dovich maps