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    Morphological signatures of mergers in the TNG50 simulation and the Kilo-Degree Survey: the merger fraction from dwarfs to Milky Way-like galaxies

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    Author
    Guzmán-Ortega, A.
    Rodriguez-Gomez, V.
    Snyder, G.F.
    Chamberlain, K.
    Hernquist, L.
    Affiliation
    University of Arizona
    Issue Date
    2022-11-17
    Keywords
    galaxies: formation
    galaxies: statistics
    galaxies: structure
    methods: numerical
    techniques: image processing
    
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    Publisher
    Oxford University Press
    Citation
    Alejandro Guzmán-Ortega, Vicente Rodriguez-Gomez, Gregory F Snyder, Katie Chamberlain, Lars Hernquist, Morphological signatures of mergers in the TNG50 simulation and the Kilo-Degree Survey: the merger fraction from dwarfs to Milky Way-like galaxies, Monthly Notices of the Royal Astronomical Society, Volume 519, Issue 4, March 2023, Pages 4920–4937, https://doi.org/10.1093/mnras/stac3334
    Journal
    Monthly Notices of the Royal Astronomical Society
    Rights
    © 2022 The Author(s) Published by Oxford University Press on behalf of Royal Astronomical Society.
    Collection Information
    This item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at repository@u.library.arizona.edu.
    Abstract
    Using the TNG50 cosmological simulation and observations from the Kilo-Degree Survey (KiDS), we investigate the connection between galaxy mergers and optical morphology in the local Universe over a wide range of galaxy stellar masses (8.5 ≤ log (M∗/M⊙) ≤ 11). To this end, we have generated over 16 000 synthetic images of TNG50 galaxies designed to match KiDS observations, including the effects of dust attenuation and scattering, and used the statmorph code to measure various image-based morphological diagnostics in the r-band for both data sets. Such measurements include the Gini-M20 and concentration-asymmetry-smoothness statistics. Overall, we find good agreement between the optical morphologies of TNG50 and KiDS galaxies, although the former are slightly more concentrated and asymmetric than their observational counterparts. Afterwards, we trained a random forest classifier to identify merging galaxies in the simulation (including major and minor mergers) using the morphological diagnostics as the model features, along with merger statistics from the merger trees as the ground truth. We find that the asymmetry statistic exhibits the highest feature importance of all the morphological parameters considered. Thus, the performance of our algorithm is comparable to that of the more traditional method of selecting highly asymmetric galaxies. Finally, using our trained model, we estimate the galaxy merger fraction in both our synthetic and observational galaxy samples, finding in both cases that the galaxy merger fraction increases steadily as a function of stellar mass. © 2022 The Author(s).
    Note
    Immediate access
    ISSN
    0035-8711
    DOI
    10.1093/mnras/stac3334
    Version
    Final Published Version
    ae974a485f413a2113503eed53cd6c53
    10.1093/mnras/stac3334
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    UA Faculty Publications

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