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

Marc Huertas-Company, Yicheng Guo, Omri Ginzburg, Christoph T. Lee, Nir Mandelker, Maxwell Metter, Joel R. Primack, Avishai Dekel, Daniel Ceverino, Sandra M. Faber, David C. Koo, Anton Koekemoer, Gregory Snyder, Mauro Giavalisco, Haowen Zhang

Research output: Contribution to journalArticlepeer-review

28 Scopus citations

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 ~1500 star forming galaxies at 1 < z<3 in the CANDELS survey as well as in images from the VELA zoom-in cosmological simulations. We show that observational effects have a dramatic impact on the derived clump properties leading to an overestimation of the clump mass up to a factor of 10, which highlights the importance of fair comparisons between observations and simulations and the limitations of current HST data to study the resolved structure of distant galaxies. After correcting for these effects with a mixture density network, we estimate that the clump stellar mass function follows a power law down to the completeness limit (107solar masses) with the majority of the clumps being less massive than 109solar masses. This is in better agreement with recent gravitational lensing based measurements. The simulations explored in this work overall reproduce the shape of the observed clump stellar mass function and clumpy fractions when confronted under the same conditions, although they tend to lie in the lower limit of the confidence intervals of the observations. This agreement suggests that most of the observed clumps are formed in situ.

Original languageAmerican English
Pages (from-to)814-835
Number of pages22
JournalMonthly Notices of the Royal Astronomical Society
Volume499
Issue number1
DOIs
StatePublished - 1 Nov 2020
Externally publishedYes

Bibliographical note

Publisher Copyright:
© 2020 Oxford University Press. All rights reserved.

Keywords

  • Galaxies: evolution
  • Galaxies: formation
  • Galaxies: irregular
  • Galaxies: star formation
  • Galaxies: structure
  • Methods: data analysis

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