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    Rights statement: This is an author-created, un-copyedited version of an article accepted for publication/published in Astrophysical Journal. IOP Publishing Ltd is not responsible for any errors or omissions in this version of the manuscript or any version derived from it. The Version of Record is available online at doi:10.3847/1538-4357/aacdaa

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A Machine-learning Method for Identifying Multiwavelength Counterparts of Submillimeter Galaxies: Training and Testing Using AS2UDS and ALESS

Research output: Contribution to journalJournal articlepeer-review

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  • Fang Xia An
  • S. M. Stach
  • Ian Smail
  • A. M. Swinbank
  • O. Almaini
  • C. Simpson
  • W. Hartley
  • D. T. Maltby
  • R. J. Ivison
  • V. Arumugam
  • E. A. Cooke
  • B. Gullberg
  • A. P. Thomson
  • Chian-Chou Chen
  • J. M. Simpson
  • J. E. Geach
  • D. Scott
  • J. S. Dunlop
  • D. Farrah
  • P. van der Werf
  • A. W. Blain
  • C. Conselice
  • M. Michałowski
  • S. C. Chapman
  • K. E. K. Coppin
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Article number101
<mark>Journal publication date</mark>1/08/2018
<mark>Journal</mark>The Astrophysical Journal
Issue number2
Volume862
Number of pages18
Publication StatusPublished
Early online date27/07/18
<mark>Original language</mark>English

Abstract

We describe the application of supervised machine-learning algorithms to identify the likely multiwavelength counterparts to submillimeter sources detected in panoramic, single-dish submillimeter surveys. As a training set, we employ a sample of 695 (S 870μm ≳ 1 mJy) submillimeter galaxies (SMGs) with precise identifications from the ALMA follow-up of the SCUBA-2 Cosmology Legacy Survey’s UKIDSS-UDS field (AS2UDS). We show that radio emission, near-/mid-infrared colors, photometric redshift, and absolute H-band magnitude are effective predictors that can distinguish SMGs from submillimeter-faint field galaxies. Our combined radio + machine-learning method is able to successfully recover ˜85% of ALMA-identified SMGs that are detected in at least three bands from the ultraviolet to radio. We confirm the robustness of our method by dividing our training set into independent subsets and using these for training and testing, respectively, as well as applying our method to an independent sample of ˜100 ALMA-identified SMGs from the ALMA/LABOCA ECDF-South Survey (ALESS). To further test our methodology, we stack the 870 μm ALMA maps at the positions of those K-band galaxies that are classified as SMG counterparts by the machine learning but do not have a >4.3σ ALMA detection. The median peak flux density of these galaxies is S 870μm = (0.61 ± 0.03) mJy, demonstrating that our method can recover faint and/or diffuse SMGs even when they are below the detection threshold of our ALMA observations. In future, we will apply this method to samples drawn from panoramic single-dish submillimeter surveys that currently lack interferometric follow-up observations to address science questions that can only be tackled with large statistical samples of SMGs.

Bibliographic note

This is an author-created, un-copyedited version of an article accepted for publication/published in Astrophysical Journal. IOP Publishing Ltd is not responsible for any errors or omissions in this version of the manuscript or any version derived from it. The Version of Record is available online at doi:10.3847/1538-4357/aacdaa