Multi-wavelength properties of radio- and machine-learning-identified counterparts to submillimeter sources in s2cosmos
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Date
2019
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Publisher
The Astrophysical Journal
Abstract
We identify multi-wavelength counterparts to 1147 submillimeter sources from the S2COSMOS SCUBA-2 survey of the COSMOS field by employing a recently developed radio+machine-learning method trained on a large sample of Atacama Large Millimeter/submillimeter Array (ALMA)–identified submillimeter galaxies (SMGs), including 260 SMGs identified in the AS2COSMOS pilot survey. In total, we identify 1222 optical/near-infrared (NIR)/radio counterparts to the 897 S2COSMOS submillimeter sources with S850 > 1.6 mJy, yielding an overall identification rate of (78 ± 9)%. We find that (22 ± 5)% of S2COSMOS sources have multiple identified counterparts. We estimate that roughly 27% of these multiple counterparts within the same SCUBA-2 error circles very likely arise from physically associated galaxies rather than line-of-sight projections by chance. The photometric redshift of our radio+machine-learning-identified SMGs ranges from z = 0.2 to 5.7 and peaks at z = 2.3 ± 0.1.
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Keywords
Galaxy evolution, Observational astronomy, Starburst galaxies, High-redshift galaxies, Galaxy formation
Citation
An, Fang & Simpson, J. & Smail, Ian & Swinbank, A. & Ma, Cong & Liu, Daizhong & Lang, P. & Schinnerer, E. & Karim, Alexander & Magnelli, B. & Leslie, Sarah & Bertoldi, F. & Chen, Chian-Chou & Geach, James & Matsuda, Yuichi & Stach, S. & Wardlow, J. & Gullberg, B. & Ivison, R. & Smith, Deshawn. (2019). Multi-wavelength Properties of Radio- and Machine-learning-identified Counterparts to Submillimeter Sources in S2COSMOS. The Astrophysical Journal. 886. 48. 10.3847/1538-4357/ab4d53.