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dc.contributor.authorAn, FangXia
dc.contributor.authorSimpson, J. M.
dc.contributor.authorSmail, Ian
dc.contributor.authorSwinbank, A. M.
dc.contributor.authorMa, Cong
dc.contributor.authorLiu, Daizhong
dc.contributor.authorLang, P.
dc.contributor.authorSchinnerer, E.
dc.contributor.authorKarim, A.
dc.contributor.authorMagnelli, B.
dc.contributor.authorLeslie, S.
dc.contributor.authorBertoldi, F.
dc.contributor.authorChen, Chian-Chou
dc.contributor.authorGeach, J. E.
dc.contributor.authorMatsuda, Y.
dc.contributor.authorStach, S. M.
dc.contributor.authorWardlow, J. L.
dc.contributor.authorGullberg, B.
dc.contributor.authorIvison, R. J.
dc.contributor.authorAo, Y.
dc.contributor.authorCoogan, R. T.
dc.contributor.authorThomson, A. P.
dc.contributor.authorChapman, S. C.
dc.contributor.authorWang, R.
dc.contributor.authorWang, Wei-Hao
dc.contributor.authorYang, Y.
dc.contributor.authorAsquith, R.
dc.contributor.authorBourne, N.
dc.contributor.authorCoppin, K.
dc.contributor.authorHine, N. K.
dc.contributor.authorHo, L. C.
dc.contributor.authorHwang, H. S.
dc.contributor.authorKato, Y.
dc.contributor.authorLacaille, K.
dc.contributor.authorLewis, A. J. R.
dc.contributor.authorOteo, I.
dc.contributor.authorScholtz, J.
dc.contributor.authorSawicki, M.
dc.contributor.authorSmith, D.
dc.date.accessioned2020-03-10T01:06:37Z
dc.date.available2020-03-10T01:06:37Z
dc.date.issued2019-11-20
dc.identifier.citationAn , F , Simpson , J M , Smail , I , Swinbank , A M , Ma , C , Liu , D , Lang , P , Schinnerer , E , Karim , A , Magnelli , B , Leslie , S , Bertoldi , F , Chen , C-C , Geach , J E , Matsuda , Y , Stach , S M , Wardlow , J L , Gullberg , B , Ivison , R J , Ao , Y , Coogan , R T , Thomson , A P , Chapman , S C , Wang , R , Wang , W-H , Yang , Y , Asquith , R , Bourne , N , Coppin , K , Hine , N K , Ho , L C , Hwang , H S , Kato , Y , Lacaille , K , Lewis , A J R , Oteo , I , Scholtz , J , Sawicki , M & Smith , D 2019 , ' Multi-wavelength properties of radio and machine-learning identified counterparts to submillimeter sources in S2COSMOS ' , The Astrophysical Journal, Volume , vol. 886 , no. 1 , 48 . https://doi.org/10.3847/1538-4357/ab4d53
dc.identifier.otherPURE: 18738219
dc.identifier.otherPURE UUID: 59ac870e-773c-48b6-8538-abb85c079a5f
dc.identifier.otherArXiv: http://arxiv.org/abs/1910.03596v1
dc.identifier.otherScopus: 85077860792
dc.identifier.otherORCID: /0000-0001-9708-253X/work/70585856
dc.identifier.otherORCID: /0000-0002-0729-2988/work/70585890
dc.identifier.urihttp://hdl.handle.net/2299/22396
dc.description© 2019 The American Astronomical Society. All rights reserved.
dc.description.abstractWe 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 zen
dc.format.extent18
dc.language.isoeng
dc.relation.ispartofThe Astrophysical Journal, Volume
dc.subjectastro-ph.GA
dc.subjectastro-ph.CO
dc.titleMulti-wavelength properties of radio and machine-learning identified counterparts to submillimeter sources in S2COSMOSen
dc.contributor.institutionCentre for Astrophysics Research
dc.contributor.institutionCentre of Data Innovation Research
dc.contributor.institutionSchool of Physics, Astronomy and Mathematics
dc.description.statusPeer reviewed
rioxxterms.versionVoR
rioxxterms.versionofrecordhttps://doi.org/10.3847/1538-4357/ab4d53
rioxxterms.typeJournal Article/Review
herts.preservation.rarelyaccessedtrue


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