Galaxy Zoo: probabilistic morphology through Bayesian CNNs and active learning
Open accessAbstract
We use Bayesian convolutional neural networks and a novel generative model of Galaxy Zoo volunteer responses to infer posteriors for the visual morphology of galaxies. Bayesian CNN can learn from galaxy images with uncertain labels and then, for previously unlabelled galaxies, predict the probability of each possible label. Our posteriors are well-calibrated (e.g. for predicting bars, we achieve coverage errors of 11.8 per cent within a vote fraction deviation of 0.2) and hence are reliable for practical use. Further, using our posteriors, we apply the active learning strategy BALD to request volunteer responses for the subset of galaxies which, if labelled, would be most informative for training our network. We show that training our Bayesian CNNs using active learning requires up to 35–60 per cent fewer labelled galaxies, depending on the morphological feature being classified. By combining human and machine intelligence, Galaxy zoo will be able to classify surveys of any conceivable scale on a time-scale of weeks, providing massive and detailed morphology catalogues to support research into galaxy evolution.
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Walmsley et al. (2020) is the first to explore Bayesian deep learning as a way to both estimate uncertainties and also identify the most informative examples.
“The work by Walmsley et al. (2020) is also the first to explore Bayesian deep learning as a way to both estimate uncertainties an also identify the most informative examples”
From The Dawes Review 10: The impact of deep learning for the analysis of galaxy surveys (2023) · cites this paper as Walmsley et al. (2020)
Section 5 (Conclusion; page not provided) says the Bayesian CNNs express uncertainty, and Section 3.5 (Application; page not provided) describes using them to identify informative galaxies for labelling. The paper's priority claim is narrower: Section 3.1 calls it the first combination of active learning with CNNs in astrophysics, not the first exploration of Bayesian deep learning for both purposes generally. (LLM, conf=0.87)
Experimental — accuracy not yet measuredActive learning allows one to select the most informative examples for the model which are the ones showed to human classifiers.
“Active learning allows one to select the most informative examples for the model which are the ones showed to human classifiers.”
From The Dawes Review 10: The impact of deep learning for the analysis of galaxy surveys (2023) · cites this paper as Walmsley et al. (2020)
Active learning has been particularly explored in the framework of galaxy morphology.
“Active learning has been particularly explored in the framework of galaxy morphology (e.g. Walmsley et al. 2020).”
From The Dawes Review 10: The impact of deep learning for the analysis of galaxy surveys (2023) · cites this paper as Walmsley et al. 2020
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Self-citationRichards, J. W., Starr, D. L., Brink, H., Miller, A. A., Bloom, J. S., Butler, N., James, J. B., Long, J. P., & Rice, J. (2011) · The Astrophysical Journal · doi:10.1088/0004-637x/744/2/192 · Cited by 74 · see citing works
Ishida, É. E. O., Beck, R. E., González-Gaitán, S., de Souza, R. S., Krone-Martins, A., Barrett, J. W., Kennamer, N., Vilalta, R., Burgess, J. M., Quint, B., et al. (2018) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/sty3015 · Cited by 74 · see citing works
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An automatic taxonomy of galaxy morphology using unsupervised machine learning
Hocking, A., Geach, J., Sun, Y., & Davey, N. (2017) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/stx2351 · Cited by 67 · see citing works
Walmsley, M., Ferguson, A. M. N., Mann, R. G., & Lintott, C. J. (2019) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/sty3232 · Cited by 66 · see citing works
Self-citationIntegrating human and machine intelligence in galaxy morphology classification tasks
Beck, M., Scarlata, C., Fortson, L., Lintott, C., Simmons, B., Galloway, M. A., Willett, K., Dickinson, H., Masters, K., Marshall, P., et al. (2018) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/sty503 · Cited by 53 · see citing works
Self-citationA transient search using combined human and machine classifications
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Self-citationWFIRST-2.4: What Every Astronomer Should KnowPreprint
Spergel, D. N., Gehrels, N. A., Breckinridge, J. B., Donahue, M. E., Dressler, A. M., Gaudi, B. S., Greene, T. P., Guyon, O., Hirata, C. M., Kalirai, J., et al. (2013) · arXiv (Cornell University) · doi:10.48550/arxiv.1305.5425 · Cited by 49 · see citing works
Deep learning at scale for the construction of galaxy catalogs in the Dark Energy Survey
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Loss-Calibrated Approximate Inference in Bayesian Neural NetworksPreprint
Cobb, A. D., Roberts, S., & Gal, Y. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1805.03901 · Cited by 32 · see citing works
Self-citationGalaxy Zoo: finding offset discs and bars in SDSS galaxies★
Kruk, S., Lintott, C. J., Simmons, B. D., Bamford, S. P., Cardamone, C. N., Fortson, L., Hart, R. E., Häußler, B., Masters, K. L., Nichol, R. C., et al. (2017) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/stx1026 · Cited by 29 · see citing works
Self-citationDoes Mary know I experience plus rather than quus? A new hard problem
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The Effect of Minor and Major Mergers on the Evolution of Low-excitation Radio Galaxies
Gordon, Y., Pimbblet, K. A., Kaviraj, S., Owers, M. S., O’Dea, C. P., Walmsley, M., Baum, S. A., Crossett, J. P., Fraser-McKelvie, A., Lintott, C. J., et al. (2019) · The Astrophysical Journal · doi:10.3847/1538-4357/ab203f · Cited by 18 · see citing works
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Systematic Labeling Bias in Galaxy Morphologies
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Physical Parameters along the Hubble Sequence
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Flagged first, then by citations
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Cited by
Sorted by citations
A review of uncertainty quantification in deep learning: Techniques, applications and challenges
Abdar, M., Pourpanah, F., Hussain, S., Rezazadegan, D., Liu, L., Ghavamzadeh, M., Fieguth, P., Cao, X., Khosravi, A., Acharya, U. R., et al. (2021) · Information Fusion · doi:10.1016/j.inffus.2021.05.008 · Cited by 2,720 · see citing works
“[460] applied Bayesian CNN and a new generative model of Galaxy Zoo volunteer responses to infer posteriors for the visual morphology of galaxies.”
MethodMorpheus: A Deep Learning Framework for the Pixel-level Analysis of Astronomical Image Data
Hausen, R., & Robertson, B. E. (2020) · The Astrophysical Journal Supplement Series · doi:10.3847/1538-4365/ab8868 · Cited by 110 · see citing works
“The efficiency of users varies, but by leveraging the power of the public interest and enthusiasm, and now machine learning (Beck et al. 2018; Walmsley et al. 2019), the project can use scalability to offset variability in the performance of individual users.”
BackgroundThe Dawes Review 10: The impact of deep learning for the analysis of galaxy surveys
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“Walmsley et al. (2019) and Tanoglidis et al. (2021b) explored the use of CNNs for the classification of low surface brightness (LSB) structures in deep imaging surveys.”
BackgroundSelf-supervised Representation Learning for Astronomical Images
Hayat, M. A., Stein, G., Harrington, P., Lukić, Z., & Mustafa, M. (2021) · The Astrophysical Journal Letters · doi:10.3847/2041-8213/abf2c7 · Cited by 79 · see citing works
Mining for Strong Gravitational Lenses with Self-supervised Learning
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Zoobot: Adaptable Deep Learning Models for Galaxy Morphology
Walmsley, M., Allen, C., Aussel, B., Bowles, M., Gregorowicz, K., Slijepcevic, I. V., Lintott, C. J., Scaife, A. M. M., Jabłońska, M., Karchev, K., et al. (2023) · Journal of Open Source Software · doi:10.21105/joss.05312 · Cited by 45 · see citing works
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Galaxy morphology classification based on Convolutional vision Transformer (CvT)
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- Authors
- Walmsley, M., Smith, L., Lintott, C., Gal, Y., Bamford, S., Dickinson, H., Fortson, L., Kruk, S., Masters, K., Scarlata, C., Simmons, B., Smethurst, R., & Wright, D.
- Affiliations
- University of Oxford (GB)
- University of Nottingham (GB)
- University of Minnesota (US)
- European Space Agency (FR)
- Haverford College (US)
- University of California San Diego (US)
- Corresponding author
- Walmsley, Mike
- ORCID
- Venue
- Monthly Notices of the Royal Astronomical Society
- Publisher
- Oxford University Press (OUP)
- Published
- 2019-10-07
- Volume
- 491
- Issue
- 2
- Pages
- 1554-1574
- ISSN
- 0035-8711 (ISSN-L)
- 1365-2966
- OpenAlex
- W2946593308
- Cited by
- 160
- References
- 109
- Citations by year
- 2026: 15 · 2025: 20 · 2024: 39 · 2023: 18 · 2022: 30 · 2021: 26 · 2020: 11 · 2019: 1
- FWCI
- 9.12
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model (vor)
- Locations
- Monthly Notices of the Royal Astronomical Society (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- Lancaster EPrints (Lancaster University) (submittedVersion)
- Repository@Nottingham (University of Nottingham) (submittedVersion, cc-by)
- arXiv (Cornell University)
- Funders
- Science and Technology Facilities Council (ST/R505006/1, ST/N003179/1)
- National Science Foundation (IIS-1619177, OAC-1835530, AST-1716602)
- Topics
- Physical Sciences › Physics and Astronomy › Astronomy and Astrophysics › Galaxies: Formation, Evolution, Phenomena
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Physics and Astronomy › Astronomy and Astrophysics › Stellar, planetary, and galactic studies
- Keywords
- Galaxy Zoo, galaxy morphology, Bayesian convolutional neural network, active learning (machine learning), citizen science, galactic bars, label uncertainty, galaxy 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.
Statistics and arithmetic are recomputed, not judged.
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