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Cited by 58Galaxies: Formation, Evolution, PhenomenaStellar, planetary, and galactic studiesAstrophysics and Star Formation Studies

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

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Ghosh, Aritra, Urry, C. Megan, Wang, Zhengdong, Schawinski, Kevin, Turp, Dennis, Powell, Meredith C.·2020·The Astrophysical Journal
DOI 10.3847/1538-4357/ab8a47
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Abstract

Abstract We examine morphology-separated color–mass diagrams to study the quenching of star formation in ∼100,000 (z ∼ 0) Sloan Digital Sky Survey (SDSS) and ∼20,000 (z ∼ 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. We first trained GaMorNet on simulations of galaxies with a bulge and a disk component and then transfer learned using ∼25% of each data set to achieve misclassification rates of ≲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 (∼14% of disks at z ∼ 0 and 2% of disks at z ∼ 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 ∼ 0 and z ∼ 1.

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  • Transfer learning can reduce the amount of labelled examples needed by a factor of a few.

    “They showed that the amount of labelled examples can be reduced by a factor of a few with this approach.”

    From The Dawes Review 10: The impact of deep learning for the analysis of galaxy surveys (2023) · cites this paper as Ghosh et al. (2020)

  • Ghosh et al. (2020) showed that with a transfer learning step, CNNs can provide reliable structural parameters for both low and high redshift galaxies.

    “Ghosh et al. (2020) built on this and showed that with a transfer learning step, CNNs can provide reliable structural parameters for both low and high redshift galaxies.”

    From The Dawes Review 10: The impact of deep learning for the analysis of galaxy surveys (2023) · cites this paper as Ghosh et al. (2020)

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Authors
Ghosh, A., Urry, C. M., Wang, Z., Schawinski, K., Turp, D., & Powell, M. C.
Affiliations
Yale University (US)
Corresponding author
Ghosh, Aritra
ORCID
Venue
The Astrophysical Journal
Publisher
American Astronomical Society
Published
2020-06-01
Volume
895
Issue
2
Pages
112
ISSN
  • 0004-637X (ISSN-L)
  • 1538-4357
DOI
10.3847/1538-4357/ab8a47
OpenAlex
W3033060214
Cited by
58
References
52
Citations by year
2026: 5 · 2025: 5 · 2024: 11 · 2023: 19 · 2022: 10 · 2021: 7 · 2020: 1
FWCI
4.12
Type
journal article
Language
en
Open access
Licence
Locations
  • The Astrophysical Journal (publishedVersion)
  • arXiv (Cornell University) (submittedVersion)
  • arXiv (Cornell University)
Funders
  • National Science Foundation (1715512)
  • National Aeronautics and Space Administration (80NSSC18K0418)
Topics
  • Physical Sciences › Physics and Astronomy › Astronomy and Astrophysics › Galaxies: Formation, Evolution, Phenomena
  • Physical Sciences › Physics and Astronomy › Astronomy and Astrophysics › Stellar, planetary, and galactic studies
  • Physical Sciences › Physics and Astronomy › Astronomy and Astrophysics › Astrophysics and Star Formation Studies
Keywords
galaxy morphology, star formation quenching, convolutional neural networks, bulge-dominated galaxies, disk-dominated galaxies, red sequence, green valley, star formation rate, SDSS galaxies, transfer learning, galaxy evolution

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