Star–galaxy classification using deep convolutional neural networks
Open accessAbstract
Most existing star–galaxy classifiers use the reduced summary information from catalogues, requiring careful feature extraction and selection. The latest advances in machine learning that use deep convolutional neural networks (ConvNets) allow a machine to automatically learn the features directly from the data, minimizing the need for input from human experts. We present a star–galaxy classification framework that uses deep ConvNets directly on the reduced, calibrated pixel values. Using data from the Sloan Digital Sky Survey and the Canada–France–Hawaii Telescope Lensing Survey, we demonstrate that ConvNets are able to produce accurate and well-calibrated probabilistic classifications that are competitive with conventional machine learning techniques. Future advances in deep learning may bring more success with current and forthcoming photometric surveys, such as the Dark Energy Survey and the Large Synoptic Survey Telescope, because deep neural networks require very little, manual feature engineering.
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CNNs offer a marginal gain over more ML feature-based approaches for star-galaxy separation.
“The general conclusion however is that CNNs offer a marginal gain over more ML feature-based approaches for this classification problem.”
From The Dawes Review 10: The impact of deep learning for the analysis of galaxy surveys (2023) · cites this paper as Kim & Brunner (2017)
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- Authors
- Kim, E. J., & Brunner, R. J.
- Affiliations
- University of Illinois Urbana-Champaign (US)
- Corresponding author
- Kim, Edward J.
- Venue
- Monthly Notices of the Royal Astronomical Society
- Publisher
- Oxford University Press (OUP)
- Published
- 2016-10-17
- Volume
- 464
- Issue
- 4
- Pages
- 4463-4475
- ISSN
- 0035-8711 (ISSN-L)
- 1365-2966
- OpenAlex
- W2515239931
- Cited by
- 221
- References
- 4
- Citations by year
- 2026: 9 · 2025: 20 · 2024: 25 · 2023: 29 · 2022: 29 · 2021: 28 · 2020: 25 · 2019: 28 · 2018: 17 · 2017: 10 · 2016: 1
- FWCI
- 8.93
- Type
- journal article
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- Monthly Notices of the Royal Astronomical Society (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- HighWire Press Open Archive (submittedVersion)
- arXiv (Cornell University)
- Funders
- National Science Foundation (AST-1313415, ACI-1053575)
- Topics
- Physical Sciences › Physics and Astronomy › Astronomy and Astrophysics › Galaxies: Formation, Evolution, Phenomena
- Physical Sciences › Engineering › Computational Mechanics › Astronomical Observations and Instrumentation
- Physical Sciences › Physics and Astronomy › Astronomy and Astrophysics › Stellar, planetary, and galactic studies
- Keywords
- star-galaxy classification, deep convolutional neural networks, probabilistic classification, photometric surveys, Sloan Digital Sky Survey, Dark Energy Survey, LSST, astronomical image classification
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