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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Billions of galaxies are virtually impossible to classify visually by humans, even using large community-based projects such as Galaxy Zoo.
“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
The Introduction says modern survey catalogues are too large for astronomers to inspect in full. The Conclusion reports that Galaxy Zoo volunteers successfully classified hundreds of thousands of galaxies, but the paper does not establish that classifying billions would be virtually impossible even with a community project. Page numbers were not provided in our copy. (LLM, conf=0.85)
Experimental — accuracy not yet measuredZoobot 2 is a Bayesian CNN pretrained on 92 million morphological classifications of galaxies from the Galaxy Zoo citizen science project.
“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
Citizen science projects such as Galaxy Zoo have demonstrated the potential for non-expert volunteers to accelerate and scale the labelling process.
“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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Mendel, J. T., Simard, L., Palmer, M. J. D., Ellison, S. L., & Patton, D. R. (2013) · The Astrophysical Journal Supplement Series · doi:10.1088/0067-0049/210/1/3 · Cited by 249 · see citing works
Galaxy Zoo: Motivations of Citizen Scientists
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PLANET HUNTERS: A TRANSITING CIRCUMBINARY PLANET IN A QUADRUPLE STAR SYSTEM
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AINUR: Atlas of Images of NUclear Rings
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Gamification in Crowdsourcing: A Review
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Galaxy Zoo: disentangling the environmental dependence of morphology and colour
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Self-citationBest of both worlds: Human-machine collaboration for object annotation
Russakovsky, O., Li, L.-J., & Fei-Fei, L. (2015) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2015.7298824 · Cited by 212 · see citing works
Prša, A., Kochoska, A., Conroy, K. E., Eisner, N. L., Hey, D., IJspeert, L., Kruse, E., Fleming, S. W., Johnston, C., Kristiansen, M. H., et al. (2022) · The Astrophysical Journal Supplement Series · doi:10.3847/1538-4365/ac324a · Cited by 207 · see citing works
Aguerri, J. A. L., Bernardi, M., Mei, S., & Alméida, J. S. (2010) · Astronomy and Astrophysics · doi:10.1051/0004-6361/201015735 · Cited by 204 · see citing works
Galaxy Zoo: the fraction of merging galaxies in the SDSS and their morphologies
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Self-citationDeep learning of multi-element abundances from high-resolution spectroscopic data
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Galaxy morphology and star formation in the Illustris Simulation at z = 0
Snyder, G. F., Torrey, P., Lotz, J. M., Genel, S., McBride, C. K., Vogelsberger, M., Pillepich, A., Nelson, D., Sales, L. V., Sijacki, D., et al. (2015) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/stv2078 · Cited by 202 · see citing works
ON THE OXYGEN AND NITROGEN CHEMICAL ABUNDANCES AND THE EVOLUTION OF THE “GREEN PEA” GALAXIES
Amorín, R., Pérez-Montero, E., & Vı́lchez, J. M. (2010) · The Astrophysical Journal Letters · doi:10.1088/2041-8205/715/2/l128 · Cited by 202 · see citing works
Darg, D., Kaviraj, S., Lintott, C. J., Schawinski, K., Sarzi, M., Bamford, S. P., Silk, J. I., Andreescu, D., Murray, P., Nichol, R. C., et al. (2009) · Monthly Notices of the Royal Astronomical Society · doi:10.1111/j.1365-2966.2009.15786.x · Cited by 201 · see citing works
Self-citationProbabilistic Random Forest: A Machine Learning Algorithm for Noisy Data Sets
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Meert, A., Vikram, V., & Bernardi, M. (2014) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/stu2333 · Cited by 198 · see citing works
Walmsley, M., Lintott, C. J., Géron, T., Kruk, S., Krawczyk, C., Willett, K., Bamford, S. P., Kelvin, L. S., Fortson, L., Gal, Y., et al. (2021) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/stab2093 · Cited by 198 · see citing works
Self-citationGalaxy Zoo: reproducing galaxy morphologies via machine learning★
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Self-citationSchawinski, K., Urry, C. M., Virani, S. N., Coppi, P., Bamford, S. P., Treister, E., Lintott, C. J., Sarzi, M., Keel, W. C., Kaviraj, S., et al. (2010) · The Astrophysical Journal · doi:10.1088/0004-637x/711/1/284 · Cited by 195 · see citing works
Self-citationBundy, K., Scarlata, C. M., Carollo, C. M., Ellis, R. S., Drory, N., Hopkins, P. F., Salvato, M., Leauthaud, A., Koekemoer, A. M., Murray, N., et al. (2010) · The Astrophysical Journal · doi:10.1088/0004-637x/719/2/1969 · Cited by 194 · see citing works
Ellison, S. L., Viswanathan, A., Patton, D. R., Bottrell, C., McConnachie, A. W., Gwyn, S., & Cuillandre, J. (2019) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/stz1431 · Cited by 192 · see citing works
Galaxy luminosities, stellar masses, sizes, velocity dispersions as a function of morphological typePreprint
Bernardi, M., Shankar, F., Hyde, J. B., Mei, S., Marulli, F., & Sheth, R. K. (2010) · Cited by 189 · see citing works
Erwin, P. (2017) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/stx3117 · Cited by 178 · see citing works
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Self-citationEllison, S. L., Sánchez, S. F., Ibarra-Medel, H. J., Antonio, B., Mendel, J. T., & Barrera-Ballesteros, J. K. (2017) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/stx2882 · Cited by 177 · see citing works
Galaxy Zoo: a sample of blue early-type galaxies at low redshift
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Self-citationThe XMM Cluster Survey: optical analysis methodology and the first data release
Mehrtens, N., Romer, A. K., Hilton, M., Lloyd-Davies, E. J., Miller, C. J., Stanford, S. A., Hosmer, M., Hoyle, B., Collins, C. A., Liddle, A. R., et al. (2012) · Monthly Notices of the Royal Astronomical Society · doi:10.1111/j.1365-2966.2012.20931.x · Cited by 175 · see citing works
Self-citationThe EFIGI catalogue of 4458 nearby galaxies with detailed morphology
Baillard, A., Bertin, E., de Lapparent, V., Fouqué, P., Arnouts, S., Mellier, Y., Pelló, R., Borgne, J. F. L., Prugniel, P., Makarov, D. I., et al. (2011) · Astronomy and Astrophysics · doi:10.1051/0004-6361/201016423 · Cited by 172 · see citing works
Illuminating Low Surface Brightness Galaxies with the Hyper Suprime-Cam Survey
Greco, J. P., Greene, J. E., Strauss, M. A., Macarthur, L. A., Flowers, X., Goulding, A. D., Huang, S., Kim, J. H., Komiyama, Y., Leauthaud, A., et al. (2018) · The Astrophysical Journal · doi:10.3847/1538-4357/aab842 · Cited by 193 · see citing works
The LOFAR Two-metre Sky Survey
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Citizen observations contributing to flood modelling: opportunities and challenges
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The SAMI Galaxy Survey: the link between angular momentum and optical morphology
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Angular momentum evolution of galaxies in EAGLE
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More, A., Cabanac, R., More, S., Alard, C., Limousin, M., Kneib, J., Gavazzi, R., & Motta, V. (2012) · The Astrophysical Journal · doi:10.1088/0004-637x/749/1/38 · Cited by 167 · see citing works
GALAXY ZOO: OBSERVING SECULAR EVOLUTION THROUGH BARS
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Self-citationGalaxy luminosities, stellar masses, sizes, velocity dispersions as a function of morphological type
Bernardi, M., Shankar, F., Hyde, J. B., Mei, S., Marulli, F., & Sheth, R. K. (2010) · Monthly Notices of the Royal Astronomical Society · doi:10.1111/j.1365-2966.2010.16425.x · Cited by 166 · see citing works
Defining and Measuring Success in Online Citizen Science: A Case Study of Zooniverse Projects
Cox, J., Oh, E. Y., Simmons, B. D., Lintott, C. J., Masters, K. L., Greenhill, A., Graham, G., & Holmes, K. (2015) · Computing in Science & Engineering · doi:10.1109/mcse.2015.65 · Cited by 162 · see citing works
Self-citationThe Galaxy Zoo survey for giant AGN-ionized clouds: past and present black hole accretion events
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Self-citationPawlik, M. M., Wild, V., Walcher, C. J., Johansson, P. H., Villforth, C., Rowlands, K., Méndez‐Abreu, J., & Hewlett, T. (2015) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/stv2878 · Cited by 162 · see citing works
Galaxy Zoo: probabilistic morphology through Bayesian CNNs and active learning
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Self-citationValentine, M., Retelny, D., To, A., Rahmati, N., Doshi, T. S., & Bernstein, M. S. (2017) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3025453.3025811 · Cited by 162 · see citing works
De novo protein design by citizen scientists
Koepnick, B., Flatten, J., Husain, T., Ford, A., Silva, D., Bick, M. J., Bauer, A., Liu, G., Ishida, Y., Boykov, A., et al. (2019) · Nature · doi:10.1038/s41586-019-1274-4 · Cited by 158 · see citing works
HOST GALAXY PROPERTIES OF THESWIFTBAT ULTRA HARD X-RAY SELECTED ACTIVE GALACTIC NUCLEUS
Koss, M., Mushotzky, R. F., Veilleux, S., Winter, L. M., Baumgartner, W. H., Tueller, J., Gehrels, N. A., & Valencic, L. A. (2011) · The Astrophysical Journal · doi:10.1088/0004-637x/739/2/57 · Cited by 156 · see citing works
Knapp, C. N., Reid, R. S., Fernández‐Giménez, M. E., Klein, J. A., & Galvin, K. A. (2019) · Sustainability · doi:10.3390/su11184899 · Cited by 155 · see citing works
Groups and clusters of galaxies in the SDSS DR8
Tempel, E., Tago, E., & Liivamägi, L. J. (2012) · Astronomy and Astrophysics · doi:10.1051/0004-6361/201118687 · Cited by 153 · see citing works
Melvin, T., Masters, K. L., Lintott, C. J., Nichol, R. C., Simmons, B. D., Bamford, S. P., Casteels, K. R. V., Cheung, E., Edmondson, E. M., Fortson, L., et al. (2014) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/stt2397 · Cited by 153 · see citing works
Self-citationCANDELS VISUAL CLASSIFICATIONS: SCHEME, DATA RELEASE, AND FIRST RESULTS
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The importance of minor-merger-driven star formation and black hole growth in disc galaxies
Kaviraj, S. (2014) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/stu338 · Cited by 150 · see citing works
Galaxy Zoo: dust in spiral galaxies★
Masters, K. L., Nichol, R., Bamford, S., Mosleh, M., Lintott, C. J., Andreescu, D., Edmondson, E. M., Keel, W. C., Murray, P., Raddick, M. J., et al. (2010) · Monthly Notices of the Royal Astronomical Society · doi:10.1111/j.1365-2966.2010.16335.x · Cited by 147 · see citing works
Self-citationA snapshot on galaxy evolution occurring in the Great Wall: the role of Nurture atz = 0
Gavazzi, G., Fumagalli, M., Cucciati, O., & Boselli, A. (2010) · Astronomy and Astrophysics · doi:10.1051/0004-6361/201014153 · Cited by 146 · see citing works
Zheng, F., Tao, R., Maier, H. R., See, L., Savic, D. A., Zhang, T., Chen, Q., Assumpção, T. H., Yang, P., Haratmeh, B. H., et al. (2018) · Reviews of Geophysics · doi:10.1029/2018rg000616 · Cited by 148 · see citing works
Crowdsourcing for bioinformatics
Good, B. M., & Su, A. I. (2013) · Bioinformatics · doi:10.1093/bioinformatics/btt333 · Cited by 144 · see citing works
Emerging technologies for biological recording
August, T., Harvey, M., Lightfoot, P., Kilbey, D., Papadopoulos, T., & Jepson, P. (2015) · Biological Journal of the Linnean Society · doi:10.1111/bij.12534 · Cited by 141 · see citing works
Surveying the reach and maturity of machine learning and artificial intelligence in astronomy
Fluke, C. J., & Jacobs, C. (2020) · WIREs Data Mining and Knowledge Discovery · doi:10.1002/widm.1349 · Cited by 149 · see citing works
Synthetic galaxy images and spectra from the Illustris simulation
Torrey, P., Snyder, G. F., Vogelsberger, M., Hayward, C. C., Genel, S., Sijacki, D., Springel, V., Hernquist, L., Nelson, D., Kriek, M., et al. (2015) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/stu2592 · Cited by 137 · see citing works
Using transfer learning to detect galaxy mergers
Ackermann, S., Schawinski, K., Zhang, C., Weigel, A. K., & Turp, M. D. (2018) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/sty1398 · Cited by 137 · see citing works
Self-citation
- 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
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