Zoobot: Adaptable Deep Learning Models for Galaxy Morphology
Open accessAbstract
Zoobot is a Python package for measuring the detailed appearance of galaxies in telescope images using deep learning.Zoobot is aimed at astronomers who want to solve a galaxy image task such as finding merging galaxies or counting spiral arms.Astronomers can use Zoobot to adapt (finetune) pretrained deep learning models to solve their task.These finetuned models perform better and require far fewer new labels than training from scratch (Walmsley, Slijepcevic, et al., 2022).
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Zoobot 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 Walmsley et al. 2023
The Abstract says Zoobot includes models pretrained on up to 92 million responses from Galaxy Zoo volunteers describing galaxy appearance. Our copy does not identify those models as “Zoobot 2” or state that they are Bayesian CNNs; it mentions Bayesian CNNs only in a reference to an earlier paper. (LLM, conf=0.86)
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- Authors
- Walmsley, M., Allen, C., Aussel, B., Bowles, M., Gregorowicz, K., Slijepcevic, I. V., Lintott, C. J., Scaife, A. M. M., Jabłońska, M., Karchev, K., Lanzieri, D., Mohan, D., O’Ryan, D., Saiguhan, B., Suárez, C., Guerra-Varas, N., & Velu, R.
- Affiliations
- University of Manchester (GB)
- University of Oxford (GB)
- Université Paris Cité (FR)
- Physical Research Laboratory (IN)
- University of Rome Tor Vergata (IT)
- Heidelberg University (DE)
- ORCID
- Venue
- Journal of Open Source Software
- Publisher
- The Open Journal
- Published
- 2023-05-08
- Volume
- 8
- Issue
- 85
- Pages
- 5312
- ISSN
- 2475-9066 (ISSN-L)
- OpenAlex
- W4381924973
- Cited by
- 45
- References
- 18
- Citations by year
- 2026: 15 · 2025: 17 · 2024: 12 · 2023: 1
- FWCI
- 5.24
- Type
- journal article
- Language
- en
- Open access
- diamond
- doi.org
- Licence
- Locations
- The Journal of Open Source Software (publishedVersion, cc-by)
- Research Explorer (The University of Manchester) (submittedVersion, cc-by)
- Funders
- National Aeronautics and Space Administration
- Vanderbilt University
- Science and Technology Facilities Council (ST/X00127X/1, ST/T506205/1, ST/X001229/1)
- 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 › Engineering › Computational Mechanics › Astronomical Observations and Instrumentation
- Keywords
- galaxy morphology, deep learning, transfer learning, pre-trained model fine-tuning, galaxy mergers, Python library
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