PMO OpenIR
A Machine-learning Method for Identifying Multiwavelength Counterparts of Submillimeter Galaxies: Training and Testing Using AS2UDS and ALESS
An, Fang Xia1,2,3; Stach, S. M.3; Smail, Ian3; Swinbank, A. M.3; Almaini, O.4; Simpson, C.5; Hartley, W.4; Maltby, D. T.4; Ivison, R. J.6,7; Arumugam, V.6,7; Wardlow, J. L.3; Cooke, E. A.3; Gullberg, B.3; Thomson, A. P.8; Chen, Chian-Chou6; Simpson, J. M.9; Geach, J. E.10; Scott, D.11; Dunlop, J. S.7; Farrah, D.12,13; van der Werf, P.14; Blain, A. W.15; Conselice, C.4; Michalowski, M.16; Chapman, S. C.17; Coppin, K. E. K.10
2018-08-01
Source PublicationASTROPHYSICAL JOURNAL
ISSN0004-637X
Volume862Issue:2Pages:18
Corresponding AuthorAn, Fang Xia(fangxiaan@pmo.ac.cn)
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-870 mu m greater than or similar to 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 + machinelearning method is able to successfully recover similar to 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 similar to 100 ALMA-identified SMGs from the ALMA/LABOCA ECDF-South Survey (ALESS). To further test our methodology, we stack the 870 mu m 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 >4.3 sigma ALMA detection. The median peak flux density of these galaxies is S-870 mu m, = (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.
Keywordobservations galaxies: evolution galaxies: formation galaxies: high-redshift galaxies: starburst submillimeter: galaxies
DOI10.3847/1538-4357/aacdaa
WOS KeywordDEEP-FIELD-SOUTH ; STAR-FORMING GALAXIES ; DEGREE EXTRAGALACTIC SURVEY ; ALMA SPECTROSCOPIC SURVEY ; PARKES SELECTED REGIONS ; NUMBER COUNTS ; HIGH-REDSHIFT ; MU-M ; MIDINFRARED COUNTERPARTS ; BOLOMETER CAMERA
Indexed BySCI
Language英语
WOS Research AreaAstronomy & Astrophysics
WOS SubjectAstronomy & Astrophysics
WOS IDWOS:000440045800002
PublisherIOP PUBLISHING LTD
Citation statistics
Cited Times:2[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://libir.pmo.ac.cn/handle/332002/21612
Collection中国科学院紫金山天文台
Corresponding AuthorAn, Fang Xia
Affiliation1.Chinese Acad Sci, Purple Mt Observ, 8 Yuanhua Rd, Nanjing 210034, Jiangsu, Peoples R China
2.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
3.Univ Durham, Dept Phys, Ctr Extragalact Astron, Durham DH1 3LE, England
4.Univ Nottingham, Sch Phys & Astron, Nottingham NG7 2RD, England
5.Northern Operat Ctr, Gemini Observ, 670 N Aohuku Pl, Hilo, HI 96720 USA
6.European Southern Observ, Karl Schwarzschild Str 2, Garching, Germany
7.Univ Edinburgh, Inst Astron, Royal Observ, Blackford Hill, Edinburgh EH9 3HJ, Midlothian, Scotland
8.Univ Manchester, Oxford Rd, Manchester M13 9PL, Lancs, England
9.Acad Sinica, Inst Astron & Astrophys, 1,Sect 4,Roosevelt Rd, Taipei 10617, Taiwan
10.Univ Hertfordshire, Sch Phys Astron & Math, Ctr Astrophys Res, Hatfield AL10 9AB, Herts, England
11.Univ British Columbia, Dept Phys & Astron, 6224 Agr Rd, Vancouver, BC V6T 1Z1, Canada
12.Univ Hawaii, 2505 Correa Rd, Honolulu, HI 96822 USA
13.Virginia Polytech Inst & State Univ, Dept Phys, MC 0435,910 Drillfield Dr, Blacksburg, VA 24061 USA
14.Leiden Univ, Leiden Observ, POB 9513, NL-2300 RA Leiden, Netherlands
15.Univ Leicester, Dept Phys & Astron, Univ Rd, Leicester LE1 7RH, Leics, England
16.Adam Mickiewicz Univ, Fac Phys, Astron Observ Inst, Ul Sloneczna 36, PL-60286 Poznan, Poland
17.Dalhousie Univ, Dept Phys & Atmospher Sci, Halifax, NS B3H 3J5, Canada
Recommended Citation
GB/T 7714
An, Fang Xia,Stach, S. M.,Smail, Ian,et al. A Machine-learning Method for Identifying Multiwavelength Counterparts of Submillimeter Galaxies: Training and Testing Using AS2UDS and ALESS[J]. ASTROPHYSICAL JOURNAL,2018,862(2):18.
APA An, Fang Xia.,Stach, S. M..,Smail, Ian.,Swinbank, A. M..,Almaini, O..,...&Coppin, K. E. K..(2018).A Machine-learning Method for Identifying Multiwavelength Counterparts of Submillimeter Galaxies: Training and Testing Using AS2UDS and ALESS.ASTROPHYSICAL JOURNAL,862(2),18.
MLA An, Fang Xia,et al."A Machine-learning Method for Identifying Multiwavelength Counterparts of Submillimeter Galaxies: Training and Testing Using AS2UDS and ALESS".ASTROPHYSICAL JOURNAL 862.2(2018):18.
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