Multi-wavelength properties of radio and machine-learning identified counterparts to submillimeter sources in S2COSMOS
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
Item Type | Article |
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Additional information | © 2019 The American Astronomical Society. All rights reserved. |
Keywords | astro-ph.ga, astro-ph.co |
Date Deposited | 15 May 2025 14:15 |
Last Modified | 31 May 2025 00:23 |
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picture_as_pdf - Xia_An_2019_ApJ_886_48.pdf
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