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Cited by 92Galaxies: Formation, Evolution, PhenomenaStellar, planetary, and galactic studiesAstrophysics and Star Formation Studies

Galaxy Merger Rates up to z ∼ 3 Using a Bayesian Deep Learning Model: A Major-merger Classifier Using IllustrisTNG Simulation Data

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Ferreira, Leonardo, Conselice, Christopher J., Duncan, Kenneth, Cheng, Ting-Yun, Griffiths, Alex, Whitney, Amy·2020·The Astrophysical Journal
DOI 10.3847/1538-4357/ab8f9b
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Abstract

Merging is potentially the dominant process in galaxy formation, yet there is still debate about its history over cosmic time. To address this, we classify major mergers and measure galaxy merger rates up to z ∼ 3 in all five CANDELS fields (UDS, EGS, GOODS-S, GOODS-N, COSMOS) using deep learning convolutional neural networks trained with simulated galaxies from the IllustrisTNG cosmological simulation. The deep learning architecture used is objectively selected by a Bayesian optimization process over the range of possible hyperparameters. We show that our model can achieve 90% accuracy when classifying mergers from the simulation and has the additional feature of separating mergers before the infall of stellar masses from post-mergers. We compare our machine-learning classifications on CANDELS galaxies and compare with visual merger classifications from Kartaltepe et al., and show that they are broadly consistent. We finish by demonstrating that our model is capable of measuring galaxy merger rates, , that are consistent with results found for CANDELS galaxies using close pairs statistics, with . This is the first general agreement between major mergers measured using pairs and structure at z < 3.

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Crossref · 10.3847/1538-4357/ab8f9b

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45 of 45 references verified0 citation errors · 0 not checkable

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  • Ferreira et al. (2020) showed that the CNNs can distinguish mergers from non-mergers based on multi-wavelength imaging with an accuracy of ~90%.

    “They showed that the CNNs can distinguish mergers from non-mergers based on multi-wavelength imaging with an accuracy of ∼ 90%.”

    From The Dawes Review 10: The impact of deep learning for the analysis of galaxy surveys (2023) · cites this paper as Ferreira et al. (2020)

  • When applied to data from the CANDELS survey, Ferreira et al. (2020) results reconcile measurements of the merger rate performed with pair counts and photometry.

    “When applied to data from the CANDELS survey, their results reconcile measurements of the merger rate performed with pair counts and photometry (see Figure 27).”

    From The Dawes Review 10: The impact of deep learning for the analysis of galaxy surveys (2023) · cites this paper as Ferreira et al. (2020)

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Authors
Ferreira, L., Conselice, C. J., Duncan, K., Cheng, T.-Y., Griffiths, A., & Whitney, A.
Affiliations
  • University of Nottingham (GB)
  • Leiden University (NL)
ORCID
Venue
The Astrophysical Journal
Publisher
American Astronomical Society
Published
2020-06-01
Volume
895
Issue
2
Pages
115
ISSN
  • 0004-637X (ISSN-L)
  • 1538-4357
DOI
10.3847/1538-4357/ab8f9b
OpenAlex
W3022194331
Cited by
92
References
45
Citations by year
2026: 7 · 2025: 15 · 2024: 18 · 2023: 14 · 2022: 18 · 2021: 17 · 2020: 3
FWCI
5.80
Type
journal article
Language
en
Open access
Licence
Locations
  • The Astrophysical Journal (publishedVersion)
  • arXiv (Cornell University) (submittedVersion)
  • Leiden Repository (Leiden University) (submittedVersion)
  • Data Archiving and Networked Services (DANS) (submittedVersion)
  • Edinburgh Research Explorer (University of Edinburgh) (submittedVersion, other-oa)
  • arXiv (Cornell University)
Funders
  • Coordenação de Aperfeiçoamento de Pessoal de Nível Superior
  • Science and Technology Facilities Council
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 › Physics and Astronomy › Astronomy and Astrophysics › Astrophysics and Star Formation Studies
Keywords
galaxy mergers, merger rate, high-redshift galaxies, CANDELS fields, IllustrisTNG simulations, convolutional neural networks, Bayesian optimization, hyperparameter optimization, post-merger galaxies, galaxy pairs, galaxy formation

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