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Cited by 382Galaxies: Formation, Evolution, PhenomenaAdvanced Neural Network ApplicationsStellar, planetary, and galactic studies

Improving galaxy morphologies for SDSS with Deep Learning

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Domínguez Sánchez, H, Huertas-Company, M, Bernardi, M, Tuccillo, D, Fischer, J L·2018·Monthly Notices of the Royal Astronomical Society
DOI 10.1093/mnras/sty338
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Abstract

We present a morphological catalogue for ∼670 000 galaxies in the Sloan Digital Sky Survey in two flavours: T-type, related to the Hubble sequence, and Galaxy Zoo 2 (GZ2 hereafter) classification scheme. By combining accurate existing visual classification catalogues with machine learning, we provide the largest and most accurate morphological catalogue up to date. The classifications are obtained with Deep Learning algorithms using Convolutional Neural Networks (CNNs). We use two visual classification catalogues, GZ2 and Nair & Abraham (2010), for training CNNs with colour images in order to obtain T-types and a series of GZ2 type questions (disc/features, edge-on galaxies, bar signature, bulge prominence, roundness, and mergers). We also provide an additional probability enabling a separation between pure elliptical (E) from S0, where the T-type model is not so efficient. For the T-type, our results show smaller offset and scatter than previous models trained with support vector machines. For the GZ2 type questions, our models have large accuracy (>97 per cent), precision and recall values (>90 per cent), when applied to a test sample with the same characteristics as the one used for training. The catalogue is publicly released with the paper.

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  1. Barchi P. H., da Costa F. G., Sautter R., Moura T. C., Stalder D. H., Rosa R. R., de Carvalho R. R., 2017, preprint, ( arXiv:1705.06818 ) · arXiv
  2. Ciresan D., Meier U., Schmidhuber J., 2012, in Computer Vision and Pattern Recognition (CVPR), 2012 IEEE Conference on. pp 3642-3649
  3. Hinton G. E., Srivastava N., Krizhevsky A., Sutskever I., Salakhutdinov R., 2012, CoRR, abs/1207.0580 · arXiv
  4. Nair V., Hinton G. E., 2010, in F ˜ Aijrnkranz J., Joachims T., eds, Proceedings of the 27th International Conference on Machine Learning (ICML-10). Omnipress, pp 807-814, http://www. icml2010.org/papers/432.pdf
  5. Russakovsky O., et al., 2015, International Journal of Computer Vision (IJCV), 115, 211
  6. Srivastava N., Hinton G. E., Krizhevsky A., Sutskever I., Salakhutdinov R., 2014, Journal of Machine Learning Research, 15, 1929

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Authors
Domínguez Sánchez, H., Huertas-Company, M., Bernardi, M., Tuccillo, D., & Fischer, J. L.
Affiliations
  • Department of Physics and Astronomy, University of Pennsylvania, 209 South 33rd Street, Philadelphia, PA 19104, USA
  • Centre National de la Recherche Scientifique (FR)
ORCID
Tuccillo, D
Venue
Monthly Notices of the Royal Astronomical Society
Publisher
Oxford University Press (OUP)
Published
2018-02-09
Volume
476
Issue
3
Pages
3661-3676
ISSN
  • 0035-8711 (ISSN-L)
  • 1365-2966
DOI
10.1093/mnras/sty338
OpenAlex
W2768455873
Cited by
382
References
43
Citations by year
2026: 28 · 2025: 47 · 2024: 48 · 2023: 47 · 2022: 57 · 2021: 64 · 2020: 41 · 2019: 41 · 2018: 9
FWCI
20.80
Type
journal article
Language
en
Open access
Locations
  • Monthly Notices of the Royal Astronomical Society (publishedVersion)
  • arXiv (Cornell University) (submittedVersion)
  • HAL (submittedVersion, cc-by)
  • arXiv (Cornell University)
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 morphology, convolutional neural networks, Galaxy Zoo 2, Hubble sequence, visual categorization, galactic bars, edge-on galaxies, mergers, S0 galaxies, elliptical galaxies, Sloan Digital Sky Survey, color images

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