Multi-wavelength properties of radio- and machine-learning-identified counterparts to submillimeter sources in s2cosmos
An, Fang Xia
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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.