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dc.contributor.authorAn, FangXia
dc.contributor.authorStach, S. M.
dc.contributor.authorSmail, Ian
dc.contributor.authorSwinbank, A. M.
dc.contributor.authorAlmaini, O.
dc.contributor.authorHartley, W.
dc.contributor.authorMaltby, D. T.
dc.contributor.authorIvison, R. J.
dc.contributor.authorArumugam, V.
dc.contributor.authorWardlow, J. L.
dc.contributor.authorCooke, E. A.
dc.contributor.authorGullberg, B.
dc.contributor.authorChen, Chian-Chou
dc.contributor.authorGeach, J. E.
dc.contributor.authorScott, D.
dc.contributor.authorDunlop, J. S.
dc.contributor.authorFarrah, D.
dc.contributor.authorWerf, P. van der
dc.contributor.authorBlain, A. W.
dc.contributor.authorConselice, C.
dc.contributor.authorMichałowski, M. J.
dc.contributor.authorChapman, S. C.
dc.contributor.authorCoppin, K. E. K.
dc.date.accessioned2019-01-08T17:15:08Z
dc.date.available2019-01-08T17:15:08Z
dc.date.issued2018-07-27
dc.identifier.citationAn , F , Stach , S M , Smail , I , Swinbank , A M , Almaini , O , Hartley , W , Maltby , D T , Ivison , R J , Arumugam , V , Wardlow , J L , Cooke , E A , Gullberg , B , Chen , C-C , Geach , J E , Scott , D , Dunlop , J S , Farrah , D , Werf , P V D , Blain , A W , Conselice , C , Michałowski , M J , Chapman , S C & Coppin , K E K 2018 , ' A machine-learning method for identifying multi-wavelength counterparts of submillimeter galaxies : training and testing using AS2UDS and ALESS ' , The Astrophysical Journal , vol. 862 , no. 2 , 101 . https://doi.org/10.3847/1538-4357/aacdaa
dc.identifier.issn0004-637X
dc.identifier.otherPURE: 16086186
dc.identifier.otherPURE UUID: 99459a86-086e-4f0b-bbb9-a00de1fd2942
dc.identifier.otherArXiv: http://arxiv.org/abs/1806.06859v1
dc.identifier.otherScopus: 85051490213
dc.identifier.otherORCID: /0000-0002-0729-2988/work/53692742
dc.identifier.urihttp://hdl.handle.net/2299/20933
dc.description25 pages, 10 figures, three tables, accepted for publication in ApJ
dc.description.abstractWe describe the application of supervised machine-learning algorithms to identify the likely multiwavelength counterparts to submillimeter sources detected in panoramic, single-dish submillimeter surveys. As a training set, we employ a sample of 695 (S 870μm ≳ 1 mJy) submillimeter galaxies (SMGs) with precise identifications from the ALMA follow-up of the SCUBA-2 Cosmology Legacy Survey's UKIDSS-UDS field (AS2UDS). We show that radio emission, near-/mid-infrared colors, photometric redshift, and absolute H-band magnitude are effective predictors that can distinguish SMGs from submillimeter-faint field galaxies. Our combined radio + machine-learning method is able to successfully recover ∼85% of ALMA-identified SMGs that are detected in at least three bands from the ultraviolet to radio. We confirm the robustness of our method by dividing our training set into independent subsets and using these for training and testing, respectively, as well as applying our method to an independent sample of ∼100 ALMA-identified SMGs from the ALMA/LABOCA ECDF-South Survey (ALESS). To further test our methodology, we stack the 870 μm ALMA maps at the positions of those K-band galaxies that are classified as SMG counterparts by the machine learning but do not have a >4.3σ ALMA detection. The median peak flux density of these galaxies is S 870μm = (0.61 ± 0.03) mJy, demonstrating that our method can recover faint and/or diffuse SMGs even when they are below the detection threshold of our ALMA observations. In future, we will apply this method to samples drawn from panoramic single-dish submillimeter surveys that currently lack interferometric follow-up observations to address science questions that can only be tackled with large statistical samples of SMGs.en
dc.language.isoeng
dc.relation.ispartofThe Astrophysical Journal
dc.rightsOpen
dc.subjectcosmology: observations
dc.subjectgalaxies: evolution
dc.subjectgalaxies: formation
dc.subjectgalaxies: high-redshift
dc.subjectgalaxies: starburst
dc.subjectsubmillimeter: galaxies
dc.subjectAstronomy and Astrophysics
dc.subjectSpace and Planetary Science
dc.titleA machine-learning method for identifying multi-wavelength counterparts of submillimeter galaxies : training and testing using AS2UDS and ALESSen
dc.contributor.institutionCentre for Astrophysics Research
dc.contributor.institutionSchool of Physics, Astronomy and Mathematics
dc.contributor.institutionCentre of Data Innovation Research
dc.description.statusPeer reviewed
dc.identifier.urlhttp://www.scopus.com/inward/record.url?scp=85051490213&partnerID=8YFLogxK
dc.relation.schoolSchool of Physics, Astronomy and Mathematics
dc.description.versiontypeFinal Accepted Version
dcterms.dateAccepted2018-07-27
rioxxterms.versionAM
rioxxterms.versionofrecordhttps://doi.org/10.3847/1538-4357/aacdaa
rioxxterms.licenseref.uriOther
rioxxterms.typeJournal Article/Review
herts.preservation.rarelyaccessedtrue
herts.rights.accesstypeOpen


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