Galaxy Morphology Network: A Convolutional Neural Network Used to Study Morphology and Quenching in $\sim 100,000$ SDSS and $\sim 20,000$ CANDELS Galaxies

Abstract

We examine morphology-separated color-mass diagrams to study the quenching of star formation in $\sim 100,000$ ($z\sim 0$) Sloan Digital Sky Survey (SDSS) and $\sim 20,000$ ($z\sim 1$) Cosmic Assembly Near-Infrared Deep Extragalactic Legacy Survey (CANDELS) galaxies. To classify galaxies morphologically, we developed Galaxy Morphology Network (GaMorNet), a convolutional neural network that classifies galaxies according to their bulge-to-total light ratio. GaMorNet does not need a large training set of real data and can be applied to data sets with a range of signal-to-noise ratios and spatial resolutions. GaMorNet's source code as well as the trained models are made public as part of this work ( http://www.astro.yale.edu/aghosh/gamornet.html ). We first trained GaMorNet on simulations of galaxies with a bulge and a disk component and then transfer learned using $\sim 25\%$ of each data set to achieve misclassification rates of $\lesssim 5\%$. The misclassified sample of galaxies is dominated by small galaxies with low signal-to-noise ratios. Using the GaMorNet classifications, we find that bulge- and disk-dominated galaxies have distinct color-mass diagrams, in agreement with previous studies. For both SDSS and CANDELS galaxies, disk-dominated galaxies peak in the blue cloud, across a broad range of masses, consistent with the slow exhaustion of star-forming gas with no rapid quenching. A small population of red disks is found at high mass ($\sim 14\%$ of disks at $z\sim 0$ and $2\%$ of disks at $z\sim 1$). In contrast, bulge-dominated galaxies are mostly red, with much smaller numbers down toward the blue cloud, suggesting rapid quenching and fast evolution across the green valley. This inferred difference in quenching mechanism is in agreement with previous studies that used other morphology classification techniques on much smaller samples at $z\sim 0$ and $z\sim 1$.

23 pages, 12 figures; Accepted for publication in The Astrophysical Journal; Public Data Release at http://www.astro.yale.edu/aghosh/gamornet.html

References in corpus (43)

• Deep Learning in Neural Networks: An Overview
• The Sloan Digital Sky Survey: Technical Summary
• Seven-Year Wilkinson Microwave Anisotropy Probe (WMAP) Observations: Cosmological Interpretation
• The Dust Content and Opacity of Actively Star-Forming Galaxies
• The physical properties of star forming galaxies in the low redshift universe
• Detailed Structural Decomposition of Galaxy Images
• Stellar Masses and Star Formation Histories for 10^5 Galaxies from the Sloan Digital Sky Survey
• 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
• Color Separation of Galaxy Types in the Sloan Digital Sky Survey Imaging Data
• Quantifying the bimodal color-magnitude distribution of galaxies
• Galaxy Zoo : Morphologies derived from visual inspection of galaxies from the Sloan Digital Sky Survey
• NYU-VAGC: a galaxy catalog based on new public surveys
• Nearly 5000 Distant Early-Type Galaxies in COMBO-17: a Red Sequence and its Evolution since z~1
• Galaxy Luminosity Functions to z~1: DEEP2 vs. COMBO-17 and Implications for Red Galaxy Formation
• 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
• Galaxy bimodality versus stellar mass and environment
• Constraining the Low-Mass Slope of the Star Formation Sequence at 0.5<z<2.5
• Rotation-invariant convolutional neural networks for galaxy morphology prediction
• Galaxy Zoo 1 : Data Release of Morphological Classifications for nearly 900,000 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
• 3D-HST: A wide-field grism spectroscopic survey with the Hubble Space Telescope
• A Catalog of Bulge+Disk Decompositions and Updated Photometry for 1.12 Million Galaxies in the Sloan Digital Sky Survey
• Structural Parameters of Galaxies in CANDELS
• A catalog of visual-like morphologies in the 5 CANDELS fields using deep-learning
• The Dead Sequence: A Clear Bimodality in Galaxy Colors from z=0 to z=2.5
• Star-galaxy Classification Using Deep Convolutional Neural Networks
• Galaxy Zoo: Reproducing Galaxy Morphologies Via Machine Learning
• GALAPAGOS: From Pixels to Parameters
• Galaxy Types in the Sloan Digital Sky Survey Using Supervised Artificial Neural Networks
• Using transfer learning to detect galaxy mergers
• Galaxy Zoo: The large-scale spin statistics of spiral galaxies in the Sloan Digital Sky Survey
• Galaxy Zoo: Quantitative Visual Morphological Classifications for 48,000 galaxies from CANDELS
• Deep learning for galaxy surface brightness profile fitting
• Dust-Corrected Colors Reveal Bimodality in AGN Host Galaxy Colors at z~1
• The different star-formation histories of blue and red spiral and elliptical galaxies
• Detection of Bars in Galaxies using a Deep Convolutional Neural Network
• The Accuracy of Morphological Decomposition of Active Galactic Nucleus Host Galaxies
• Morphological Classification of Galaxies by Shapelet Decomposition in the Sloan Digital Sky Survey
• NoSOCS in SDSS. V. Red Disc and Blue Bulge Galaxies Across Different Environments
• Morphology and the Color-Mass Diagram as Clues to Galaxy Evolution at z~1

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• Bayesian and Convolutional Networks for Hierarchical Morphological Classification of Galaxies
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• A post-hypercritical accretion small-scale dynamo in newborn neutron stars
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