Multi-wavelength properties of radio and machine-learning identified counterparts to submillimeter sources in S2COSMOS

FangXia An, J. M. Simpson, Ian Smail, A. M. Swinbank, Cong Ma, Daizhong Liu, P. Lang, E. Schinnerer, A. Karim, B. Magnelli, S. Leslie, F. Bertoldi, Chian-Chou Chen, J. E. Geach, Y. Matsuda, S. M. Stach, J. L. Wardlow, B. Gullberg, R. J. Ivison, Y. AoR. T. Coogan, A. P. Thomson, S. C. Chapman, R. Wang, Wei-Hao Wang, Y. Yang, R. Asquith, N. Bourne, K. Coppin, N. K. Hine, L. C. Ho, H. S. Hwang, Y. Kato, K. Lacaille, A. J. R. Lewis, I. Oteo, J. Scholtz, M. Sawicki, D. Smith

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We identify multi-wavelength counterparts to 1,147 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 ALMA-identified submillimeter galaxies (SMGs), including 260 SMGs identified in the AS2COSMOS pilot survey. In total, we identify 1,222 optical/near-infrared(NIR)/radio counterparts to the 897 S2COSMOS submillimeter sources with S$_{850}$>1.6mJy, yielding an overall identification rate of ($78\pm9$)%. We find that ($22\pm5$)% 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 $z=2.3\pm0.1$. The AGN fraction of our sample is ($19\pm4$)%, which is consistent with that of ALMA SMGs in the literature. Comparing with radio/NIR-detected field galaxy population in the COSMOS field, our radio+machine-learning identified counterparts of SMGs have the highest star-formation rates and stellar masses. These characteristics suggest that our identified counterparts of S2COSMOS sources are a representative sample of SMGs at z
Original languageEnglish
Article number48
Number of pages18
JournalThe Astrophysical Journal
Issue number1
Early online date19 Nov 2019
Publication statusPublished - 20 Nov 2019


  • astro-ph.GA
  • astro-ph.CO


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