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Stellar masses of giant clumps in CANDELS and simulated galaxies using machine learning

Authors :
David C. Koo
Omri Ginzburg
Anton M. Koekemoer
Nir Mandelker
Yicheng Guo
Gregory F. Snyder
Joel R. Primack
Christoph T. Lee
Maxwell Metter
Marc Huertas-Company
Haowen Zhang
Mauro Giavalisco
Daniel Ceverino
Avishai Dekel
Sandra M. Faber
Laboratoire d'Etude du Rayonnement et de la Matière en Astrophysique (LERMA (UMR_8112))
Observatoire de Paris
Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Sorbonne Université (SU)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-CY Cergy Paris Université (CY)
Sciences, Philosophie, Histoire (SPHERE (UMR_7219))
Université Paris Diderot - Paris 7 (UPD7)-Centre National de la Recherche Scientifique (CNRS)
Source :
Mon.Not.Roy.Astron.Soc., Mon.Not.Roy.Astron.Soc., 2020, 499 (1), pp.814-835. ⟨10.1093/mnras/staa2777⟩
Publication Year :
2020
Publisher :
Oxford University Press (OUP), 2020.

Abstract

A significant fraction of high redshift star-forming disc galaxies are known to host giant clumps, whose nature and role in galaxy evolution are yet to be understood. In this work we first present a new method based on neural networks to detect clumps in galaxy images. We use this method to detect clumps in the rest-frame optical and UV images of a complete sample of $\sim1500$ star forming galaxies at $1<br />Comment: Accepted for publication in MNRAS - This is the final version

Details

ISSN :
13652966 and 00358711
Volume :
499
Database :
OpenAIRE
Journal :
Monthly Notices of the Royal Astronomical Society
Accession number :
edsair.doi.dedup.....a1e182594a450d0c79f7cead8751a50b