A CATALOG OF VISUAL-LIKE MORPHOLOGIES IN THE 5 CANDELS FIELDS USING DEEP LEARNING
Open accessAbstract
We present a catalog of visual like H-band morphologies of $\sim50.000$ galaxies ($H_{f160w}<24.5$) in the 5 CANDELS fields (GOODS-N, GOODS-S, UDS, EGS and COSMOS). Morphologies are estimated with Convolutional Neural Networks (ConvNets). The median redshift of the sample is $\sim1.25$. The algorithm is trained on GOODS-S for which visual classifications are publicly available and then applied to the other 4 fields. Following the CANDELS main morphology classification scheme, our model retrieves the probabilities for each galaxy of having a spheroid, a disk, presenting an irregularity, being compact or point source and being unclassifiable. ConvNets are able to predict the fractions of votes given a galaxy image with zero bias and $\sim10\%$ scatter. The fraction of miss-classifications is less than $1\%$. Our classification scheme represents a major improvement with respect to CAS (Concentration-Asymmetry-Smoothness)-based methods, which hit a $20-30\%$ contamination limit at high z. The catalog is released with the present paper via the $\href{http://rainbowx.fis.ucm.es/Rainbow_navigator_public}{Rainbow\,database}$
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Huertas-Company et al. (2015) applied a similar CNN architecture to high redshift HST galaxies, demonstrating similar improvement compared to other approaches including feature-based machine learning.
“Soon after, Huertas-Company et al. (2015) applied a similar architecture to high redshift galaxies observed with the Hubble Space Telescope (HST), demonstrating again a similar improvement as compared to other approaches, including feature-based Machine Learning.”
From The Dawes Review 10: The impact of deep learning for the analysis of galaxy surveys (2023) · cites this paper as Huertas-Company et al. 2015
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- Authors
- Huertas-Company, M., Gravet, R., Cabrera-Vives, G., Pérez-González, P. G., Kartaltepe, J. S., Barro, G., Bernardi, M., Mei, S., Shankar, F., Dimauro, P., Bell, E. F., Kocevski, D., Koo, D. C., Faber, S. M., & Mcintosh, D. H.
- ORCID
- Venue
- The Astrophysical Journal Supplement Series
- Publisher
- American Astronomical Society
- Published
- 2015-10-26
- Volume
- 221
- Issue
- 1
- Pages
- 8
- ISSN
- 0067-0049 (ISSN-L)
- 1538-4365
- OpenAlex
- W2198074945
- Cited by
- 285
- References
- 0
- Citations by year
- 2026: 18 · 2025: 15 · 2024: 32 · 2023: 36 · 2022: 36 · 2021: 33 · 2020: 27 · 2019: 38 · 2018: 22 · 2017: 11 · 2016: 16 · 2015: 1
- FWCI
- 9.15
- Type
- journal article
- Language
- en
- Open access
- green
- arxiv.org
- Licence
- Locations
- The Astrophysical Journal Supplement Series (publishedVersion)
- ePrints Soton (University of Southampton) (acceptedVersion)
- arXiv (Cornell University) (submittedVersion)
- LA Referencia (Red Federada de Repositorios Institucionales de Publicaciones Científicas) (submittedVersion, other-oa)
- Library Open Repository (Universidad Complutense Madrid) (submittedVersion, other-oa)
- UKnowledge (University of Kentucky) (submittedVersion)
- HAL (submittedVersion)
- Americanae (AECID Library) (submittedVersion, other-oa)
- Universidad de Chile (submittedVersion, cc-by-nc-nd)
- arXiv (Cornell University)
- Funders
- National Science Foundation (0808133)
- National Aeronautics and Space Administration (GO-12060, HST-GO-12060)
- Institut Universitaire de France
- Science and Technology Facilities Council (ST/M001326/1)
- Comisión Nacional de Investigación Científica y Tecnológica
- 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
- CANDELS fields, galaxy morphology, deep learning, convolutional neural networks, galaxy classification, Hubble Deep Field, Chandra Deep Field South, Chandra Deep Field North, CANDELS
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