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

22 Scopus citations


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
Issue number1
StatePublished - 1 Nov 2020
Externally publishedYes

Bibliographical note

Funding Information:
This material is based upon CT Lee’s research supported by the Chateaubriand Fellowship of the Office for Science & Technology of the Embassy of France in the United States. The research is also supported by a Google Faculty Grant awarded to Prof. Joel Primack. MHC is grateful to F. Lanusse for enlightening discussions about Bayesian statistics with neural networks. NM acknowledges support from the Klauss Tschira Foundation through the HITS Yale Program in Astrophysics (HYPA). We acknowledge use of observations with the NASA/ESA Hubble Space Telescope obtained from the MAST Data Archive at the Space Telescope Science Institute, which is operated by the Association of Universities for Research in Astronomy, incorporated under NASA contract NAS5-26555. Support for Program number HST-AR-15798 was provided through a grant from the STScI under NASA contract NAS5-26555. The work of AD and OG was partly supported by the grants BSF 2014-273, NSF AST-1405962, GIF I-1341-303.7/2016, and DIP STE1869/2-1 GE625/17-1.

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


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


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