A machine-learning method for identifying multi-wavelength counterparts of submillimeter galaxies : training and testing using AS2UDS and ALESS
Stach, S. M.
Swinbank, A. M.
Maltby, D. T.
Ivison, R. J.
Wardlow, J. L.
Cooke, E. A.
Geach, J. E.
Dunlop, J. S.
Werf, P. van der
Blain, A. W.
Michałowski, M. J.
Chapman, S. C.
Coppin, K. E. K.
We 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.
Published inThe Astrophysical Journal
RelationsSchool of Physics, Astronomy and Mathematics
MetadataShow full item record
Showing items related by title, author, creator and subject.
The H alpha galaxy survey. I. The galaxy sample, H alpha narrow-band observations and star formation parameters for 334 galaxies James, P.A.; Shane, N.S.; Beckman, J.E.; Cardwell, A.; Collins, C.A.; Etherton, J.; de Jong, R.S.; Fathi, K.; Knapen, J.; Peletier, R.F.; Percival, S.M.; Pollacco, D.L.; Seigar, M.S.; Stedman, S. (2004)We discuss the selection and observations of a large sample of nearby galaxies, which we are using to quantify the star formation activity in the local Universe. The sample consists of 334 galaxies across all Hubble types ...
A sample of radio galaxies spanning three decades in radio luminosity - I. The host-galaxy properties and black-hole masses McLure, R.J.; Willott, C.J.; Jarvis, M.J.; Rawlings, S.; Hill, G.J.; Mitchell, E.; Dunlop, J.S.; Wold, M. (2004)
Simmons, B. D.; Lintott, Chris; Willett, Kyle W.; Masters, Karen L.; Kartaltepe, Jeyhan S.; Häußler, Boris; Kaviraj, Sugata; Krawczyk, Coleman; Kruk, S. J.; McIntosh, Daniel H.; Smethurst, R. J.; Nichol, Robert C.; Scarlata, Claudia; Schawinski, Kevin; Conselice, Christopher J.; Almaini, Omar; Ferguson, Henry C.; Fortson, Lucy; Hartley, William; Kocevski, Dale; Koekemoer, Anton M.; Mortlock, Alice; Newman, Jeffrey A.; Bamford, Steven P.; Grogin, N. A.; Lucas, Ray A.; Hathi, Nimish P.; McGrath, Elizabeth; Peth, Michael; Pforr, Janine; Rizer, Zachary; Wuyts, Stijn; Barro, Guillermo; Bell, Eric F.; Castellano, Marco; Dahlen, Tomas; Ownsworth, Avishai Dekel Jamie; Faber, Sandra M.; Finkelstein, Steven L.; Fontana, Adriano; Galametz, Audrey; Grützbauch, Ruth; Koo, David; Lotz, Jennifer; Mobasher, Bahram; Mozena, Mark; Salvato, Mara; Wiklind, Tommy (2017-02-01)We present quantified visual morphologies of approximately 48,000 galaxies observed in three Hubble Space Telescope legacy fields by the Cosmic And Near-infrared Deep Extragalactic Legacy Survey (CANDELS) and classified ...