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Cited by 93Galaxies: Formation, Evolution, PhenomenaStellar, planetary, and galactic studiesAdvanced Neural Network Applications

Deep learning for galaxy surface brightness profile fitting

Open access
Tuccillo, D, Huertas-Company, M, Decencière, E, Velasco-Forero, S, Domínguez Sánchez, H, Dimauro, P·2018·Monthly Notices of the Royal Astronomical Society
DOI 10.1093/mnras/stx3186
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Abstract

Numerous ongoing and future large area surveys (e.g.Dark Energy Survey, EUCLID, Large Synoptic Survey Telescope, Wide Field Infrared Survey Telescope) will increase by several orders of magnitude the volume of data that can be exploited for galaxy morphology studies.The full potential of these surveys can be unlocked only with the development of automated, fast, and reliable analysis methods.In this paper, we present DeepLeGATo, a new method for 2-D photometric galaxy profile modelling, based on convolutional neural networks.Our code is trained and validated on analytic profiles (HST/CANDELS F160W filter) and it is able to retrieve the full set of parameters of one-component Sérsic models: total magnitude, effective radius, Sérsic index, and axis ratio.We show detailed comparisons between our code and GALFIT.On simulated data, our method is more accurate than GALFIT and ∼3000 time faster on GPU (∼50 times when running on the same CPU).On real data, DeepLeGATo trained on simulations behaves similarly to GALFIT on isolated galaxies.With a fast domain adaptation step made with the 0.1-0.8 per cent the size of the training set, our code is easily capable to reproduce the results obtained with GALFIT even on crowded regions.DeepLeGATo does not require any human intervention beyond the training step, rendering it much automated than traditional profiling methods.The development of this method for more complex models (two-component galaxies, variable point spread function, dense sky regions) could constitute a fundamental tool in the era of big data in astronomy.

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40 of 40 references verified0 citation errors · 0 not checkable

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  • Tuccillo et al. (2018) first used a CNN to estimate galaxy structural parameters.

    “Tuccillo et al. (2018) first used a CNN to estimate galaxy structural parameters.”

    From The Dawes Review 10: The impact of deep learning for the analysis of galaxy surveys (2023) · cites this paper as Tuccillo et al. (2018)

  • Tuccillo et al. (2018) proposed a transfer learning step using measurements performed with standard approaches; although the results quickly improve, the final results necessarily propagate the systematics of existing software, which cannot be improved by construction.

    “The authors proposed a transfer learning step using measurements performed with standard approaches. Although the results quickly improve, the final results necessarily propagate the systematics of existing software, which cannot be improved by construction.”

    From The Dawes Review 10: The impact of deep learning for the analysis of galaxy surveys (2023) · cites this paper as Tuccillo et al. (2018)

  • Tuccillo et al. (2018) did not include foreground and background galaxies in the training set, which constitutes an important difference with observations.

    “In particular, the authors did not include foreground and background galaxies in the training set which constitutes an important difference with observations.”

    From The Dawes Review 10: The impact of deep learning for the analysis of galaxy surveys (2023) · cites this paper as Tuccillo et al. (2018)

  • Tuccillo et al. (2018) used a simple training set made of analytic profiles with noise added and demonstrated that CNNs achieve comparable or better performance than standard methods, with the key advantage of being a factor of ~50 faster.

    “In this first work, the authors used a simple training set made of analytic profiles with noise added and demonstrated that CNNs achieve comparable or better performance than standard methods, with the key advantage of being a factor of ∼ 50 faster.”

    From The Dawes Review 10: The impact of deep learning for the analysis of galaxy surveys (2023) · cites this paper as Tuccillo et al. (2018)

  • Tuccillo et al. (2018) attempted to apply the trained CNNs to observed galaxies with HST, but because the training set was too simple, the results did not appear to be satisfactory.

    “Tuccillo et al. (2018) also attempted to apply the trained CNNs to observed galaxies with HST. However, because the training set was too simple, the results did not appear to be satisfactory.”

    From The Dawes Review 10: The impact of deep learning for the analysis of galaxy surveys (2023) · cites this paper as Tuccillo et al. (2018)

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Authors
Tuccillo, D., Huertas-Company, M., Decencière, E., Velasco-Forero, S., Domínguez Sánchez, H., & Dimauro, P.
Affiliations
  • Centre National de la Recherche Scientifique (FR)
  • Université Paris Sciences et Lettres (FR)
ORCID
Venue
Monthly Notices of the Royal Astronomical Society
Publisher
Oxford University Press (OUP)
Published
2017-12-11
Volume
475
Issue
1
Pages
894-909
ISSN
  • 0035-8711 (ISSN-L)
  • 1365-2966
DOI
10.1093/mnras/stx3186
OpenAlex
W2767770039
Cited by
93
References
40
Citations by year
2026: 2 · 2025: 6 · 2024: 8 · 2023: 9 · 2022: 20 · 2021: 15 · 2020: 10 · 2019: 17 · 2018: 6
FWCI
4.94
Type
journal article
Language
en
Open access
Locations
  • Monthly Notices of the Royal Astronomical Society (publishedVersion)
  • arXiv (Cornell University) (submittedVersion)
  • HAL (submittedVersion, other-oa)
  • arXiv (Cornell University)
Topics
  • Physical Sciences › Physics and Astronomy › Astronomy and Astrophysics › Galaxies: Formation, Evolution, Phenomena
  • Physical Sciences › Physics and Astronomy › Astronomy and Astrophysics › Stellar, planetary, and galactic studies
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
Keywords
galaxy surface brightness profiles, profile fitting, convolutional neural networks, Sérsic models, domain adaptation, large-area surveys

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