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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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Jiménez, R., Slosar, A., Verde, L., Bamford, S. P., Lintott, C. J., Schawinski, K., Nichol, R. C., Andreescu, D., Land, K., Murray, P., et al. (2010) · Monthly Notices of the Royal Astronomical Society · doi:10.1111/j.1365-2966.2010.16336.x · Cited by 15
Self-citationCrowdsourcing quality control for Dark Energy Survey images
Melchior, P., Sheldon, E., Drlica-Wagner, A., Rykoff, E., Abbott, T., Abdalla, F., Allam, S., Benoit-Lévy, A., Brooks, D., Buckley-Geer, E., et al. (2015) · Astronomy and Computing · doi:10.1016/j.ascom.2016.04.003 · Cited by 13
Guo, R., Hao, C.-N., Xia, X. Y., Mao, S., & Shi, Y. (2016) · The Astrophysical Journal · doi:10.3847/0004-637x/826/1/30 · Cited by 15
Discovery of a low-luminosity spiral DRAGN
Mulcahy, D. D., Mao, M., Mitsuishi, I., Scaife, A. M. M., Clarke, A. O., Babazaki, Y., Kobayashi, H., SUGANUMA, R., Matsumoto, H., & Tawara, Y. (2016) · Astronomy and Astrophysics · doi:10.1051/0004-6361/201629536 · Cited by 15
Web information monitoring and crowdsourcing for promoting and enhancing the Algerian geoheritage
Annad, O., Bendaoud, A., & Goria, S. (2017) · Arabian Journal of Geosciences · doi:10.1007/s12517-017-3061-6 · Cited by 15
AGN photoionization of gas in companion galaxies as a probe of AGN radiation in time and direction
Keel, W. C., Bennert, V. N., Pancoast, A., Harris, C., Nierenberg, A. M., Chojnowski, S. D., Moiseev, A. V., Oparin, D. V., Lintott, C. J., Schawinski, K., et al. (2018) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/sty3332 · Cited by 15
Self-citationLarsen, K. R. T., Hekler, E. B., Paul, M. J., & Gibson, B. (2020) · Communications of the Association for Information Systems · doi:10.17705/1cais.04601 · Cited by 14
Data mining for microbiologists
Hallinan, J. S. (2012) · Methods in microbiology · doi:10.1016/b978-0-08-099387-4.00002-8 · Cited by 19
Big Data for Advancing Dementia Research
Deetjen, U., Meyer, E. T., & Schroeder, R. (2015) · OECD digital economy papers · doi:10.1787/5js4sbddf7jk-en · Cited by 15
Detecting galaxy tidal features using self-supervised representation learning
Desmons, A., Brough, S., & Lanusse, F. (2024) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/stae1402 · Cited by 15
Rustige, L., Kummer, J., Griese, F., Borras, K., Brüggen, M., Connor, P. L. S., Gaede, F., Kasieczka, G., Knopp, T., & Schleper, P. (2023) · RAS Techniques and Instruments · doi:10.1093/rasti/rzad016 · Cited by 14
Euclid Collaboration, Lines, N. E. P., Collett, T. E., Walmsley, M., Rojas, K., Li, T., Leuzzi, L., Manjón-García, A., Vincken, S. H., Wilde, J., et al. (2026) · Astronomy & Astrophysics · doi:10.1051/0004-6361/202554542 · Cited by 14
Self-citationSituating Machine Intelligence Within the Cognitive Ecology of the Internet
Smart, P. R. (2017) · Minds and Machines · doi:10.1007/s11023-016-9416-z · Cited by 16
Host galaxy and orientation differences between different AGN types
Gkini, A., Plionis, M., Chira, M., & Koulouridis, E. (2021) · Astronomy and Astrophysics · doi:10.1051/0004-6361/202140278 · Cited by 14
SDSS-IV MaNGA: The incidence of major mergers in type I and II AGN host galaxies in the DR15 sample
Hernández-Toledo, H. M., Cortes-Suárez, E., Vázquez-Mata, J. A., Nevin, R., Ávila-Reese, V., Ibarra-Medel, H. J., & Negrete, C. A. (2023) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/stad1425 · Cited by 14
Nagam, B. C., Koopmans, L. V. E., Valentijn, E. A., Kleijn, G. V., de Jong, J. T. A., Napolitano, N. R., Li, R., & Tortora, C. (2023) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/stad1623 · Cited by 18
Nedkova, K., Häußler, B., Marchesini, D., Brammer, G., Feinstein, A. D., Johnston, E. J., Kartaltepe, J. S., Koekemoer, A. M., Martis, N. S., Muzzin, A., et al. (2024) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/stae1702 · Cited by 14
Arcanjo, J. S., da Luz, E. F. P., Fazenda, Á. L., & Ramos, F. M. (2015) · Future Generation Computer Systems · doi:10.1016/j.future.2015.07.005 · Cited by 14
Raddick, M. J., & Szalay, A. S. (2010) · Science · doi:10.1126/science.1186936 · Cited by 14
Self-citationThe Hα Dots Survey. II. A Second List of Faint Emission-line Objects
Salzer, J. J., Feddersen, J. R., Derloshon, K., Gronwall, C. A., Sistine, A. V., Sugden, A. U., Janowiecki, S., Hirschauer, A. S., & Kellar, J. A. (2020) · The Astronomical Journal · doi:10.3847/1538-3881/abba11 · Cited by 14
Welikala, N., Connolly, A. J., Hopkins, A. M., & Scranton, R. (2009) · The Astrophysical Journal · doi:10.1088/0004-637x/701/2/994 · Cited by 14
Torre, M., Nakayama, S., Tolbert, T. J., & Porfiri, M. (2019) · PLoS ONE · doi:10.1371/journal.pone.0211907 · Cited by 15
Investigating Clumpy Galaxies in the Sloan Digital Sky Survey Stripe 82 Using the Galaxy Zoo
Mehta, V., Scarlata, C. M., Fortson, L., Dickinson, H. J., Adams, D., Chevallard, J., Charlot, S., Beck, M., Kruk, S., & Simmons, B. D. (2021) · The Astrophysical Journal · doi:10.3847/1538-4357/abed5b · Cited by 14
Machine Learning and CosmologyPreprint
Dvorkin, C., Mishra-Sharma, S., Nord, B., Villar, V. A., Avestruz, C., Bechtol, K., Ćiprijanović, A., Connolly, A. J., Garrison, L. H., Narayan, G., et al. (2022) · arXiv (Cornell University) · doi:10.48550/arxiv.2203.08056 · Cited by 52
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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