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Galaxy Zoo: morphologies derived from visual inspection of galaxies from the Sloan Digital Sky Survey ★
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In order to understand the formation and subsequent evolution of galaxies one must first distinguish between the two main morphological classes of massive systems: spirals and early-type systems. This paper introduces a project, Galaxy Zoo, which provides visual morphological classifications for nearly one million galaxies, extracted from the Sloan Digital Sky Survey (SDSS). This achievement was made possible by inviting the general public to visually inspect and classify these galaxies via the internet. The project has obtained more than 4 × 107 individual classifications made by ∼105 participants. We discuss the motivation and strategy for this project, and detail how the classifications were performed and processed. We find that Galaxy Zoo results are consistent with those for subsets of SDSS galaxies classified by professional astronomers, thus demonstrating that our data provide a robust morphological catalogue. Obtaining morphologies by direct visual inspection avoids introducing biases associated with proxies for morphology such as colour, concentration or structural parameters. In addition, this catalogue can be used to directly compare SDSS morphologies with older data sets. The colour–magnitude diagrams for each morphological class are shown, and we illustrate how these distributions differ from those inferred using colour alone as a proxy for morphology.
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“but billions of galaxies are virtually impossible to classify visually by humans, even using large community based projects such as Galaxy Zoo (Lintott et al. 2008; Darg et al. 2010a)”
From Detecting galaxy tidal features using self-supervised representation learning (2024) · cites this paper as Lintott et al. 2008
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“Zoobot 2 (Walmsley et al. 2023) is a Bayesian CNN that is pretrained on 92 million morphological classifications of galaxies as part of the Galaxy Zoo citizen science project (Lintott et al. 2008).”
From Euclid Quick Data Release (Q1): XXVIII. The Strong Lensing Discovery Engine C – Finding lenses with machine learning (2026) · cites this paper as Lintott et al. 2008
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“Additionally, citizen science projects such as Galaxy Zoo [8] have demonstrated the potential for non-expert volunteers to accelerate and scale the labelling process.”
From AnomalyMatch: Discovering Rare Objects of Interest with Semi-supervised and Active Learning (2025) · cites this paper as [8]
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Mattmann, C. A., Hart, A. F., Cinquini, L., Lazio, J., Khudikyan, S. E., Jones, D. L., Preston, R. A., Bennett, T., Butler, B. J., Harland, D., et al. (2013) · Advances in data mining and database management book series · doi:10.4018/978-1-4666-4699-5.ch009 · Cited by 8
On the Effectiveness of Crowd Sourcing Avian Nesting Video Analysis at Wildlife@Home
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Dissecting the morphological and spectroscopic properties of galaxies in the local Universe
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Explorative user interfaces for browsing historical maps on the Web
Simon, R., Korb, J., Sadilek, C., & Baldauf, M. (2010) · Cited by 8
Nagam, B. C., Acevedo Barroso, J. A., Wilde, J., Andika, I. T., Manjón-García, A., Pearce-Casey, R., Stern, D., Nightingale, J. W., Moustakas, L. A., McCarthy, K., et al. (2025) · Astronomy & Astrophysics · doi:10.1051/0004-6361/202554132 · Cited by 8
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A New Statistical Analysis of the Morphology of Spiral Galaxies
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Citizen Science Identification of Isolated Blue Stellar Systems in the Virgo Cluster*
Dey, S., Jones, M. G., Sand, D. J., Mazziotti, N., Janowiecki, S., Zeimann, G. R., & Bennet, P. (2025) · The Astrophysical Journal · doi:10.3847/1538-4357/adbbd8 · Cited by 7
Bayesian bulge–disc decomposition of galaxy images
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Morphology, colour–magnitude, and scaling relations of galaxies in Abell 426
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Metadata
- Authors
- Lintott, C. J., Schawinski, K., Slosar, A., Land, K., Bamford, S., Thomas, D., Raddick, M. J., Nichol, R. C., Szalay, A., Andreescu, D., Murray, P., & Vandenberg, J.
- Affiliations
- University of Oxford (GB)
- Lawrence Berkeley National Laboratory (US)
- University of Portsmouth (GB)
- Johns Hopkins University (US)
- ORCID
- Venue
- Monthly Notices of the Royal Astronomical Society
- Publisher
- Oxford University Press (OUP)
- Published
- 2008-09-21
- Volume
- 389
- Issue
- 3
- Pages
- 1179-1189
- ISSN
- 0035-8711 (ISSN-L)
- 1365-2966
- OpenAlex
- W2112193291
- Cited by
- 1,488
- References
- 36
- Citations by year
- 2026: 50 · 2025: 85 · 2024: 97 · 2023: 92 · 2022: 93 · 2021: 98 · 2020: 93 · 2019: 106 · 2018: 105 · 2017: 102 · 2016: 101 · 2015: 82 · 2014: 88 · 2013: 89 · 2012: 77
- FWCI
- 25.24
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Monthly Notices of the Royal Astronomical Society (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- National Science Foundation
- U.S. Department of Energy
- National Aeronautics and Space Administration
- Alfred P. Sloan Foundation
- Princeton University
- Ohio State University
- University of Washington
- Johns Hopkins University
- University of Pittsburgh
- Case Western Reserve University
- Drexel University
- Universität Basel
- University of Portsmouth
- New Mexico State University
- Chinese Academy of Sciences
- Max-Planck-Gesellschaft
- Max-Planck-Institut für Astrophysik
- Max-Planck-Institut für Astronomie
- Science and Technology Facilities Council (ST/F00298X/1, ST/F002335/1)
- U.S. Naval Observatory
- Fermilab
- Los Alamos National Laboratory
- 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 › Astrophysical Phenomena and Observations
- Keywords
- Galaxy Zoo, galaxy morphology, visual categorization, citizen science, Sloan Digital Sky Survey, spiral galaxies, early-type galaxies, color-magnitude relation, galaxy formation and evolution
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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