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We describe the application of the supervised machine-learning algorithms toidentify the likely multi-wavelength counterparts to submillimeter sourcesdetected in panoramic, single-dish submillimeter surveys. As a training set, weemploy a sample of 695 ($S_{\rm 870\mu m}$ >1 mJy) submillimeter galaxies(SMGs) with precise identifications from the ALMA follow-up of the SCUBA-2Cosmology Legacy Survey's UKIDSS-UDS field (AS2UDS). We show that radioemission, near-/mid-infrared colors, photometric redshift, and absolute$H$-band magnitude are effective predictors that can distinguish SMGs fromsubmillimeter-faint field galaxies. Our combined radio+machine-learning methodis able to successfully recover $\sim$85 percent of ALMA-identified SMGs whichare detected in at least three bands from the ultraviolet to radio. We confirmthe robustness of our method by dividing our training set into independentsubsets and using these for training and testing respectively, as well asapplying our method to an independent sample of $\sim$100 ALMA-identified SMGsfrom the ALMA/LABOCA ECDF-South Survey (ALESS). To further test ourmethodology, we stack the 870$\mu$m ALMA maps at the positions of those$K$-band galaxies that are classified as SMG counterparts by themachine-learning but do not have a $>$4.3$ \sigma$ ALMA detection. The medianpeak flux density of these galaxies is $S_{\rm 870\mu m}=(0.61\pm0.03)$ mJy,demonstrating that our method can recover faint and/or diffuse SMGs even whenthey are below the detection threshold of our ALMA observations. In future, wewill apply this method to samples drawn from panoramic single-dishsubmillimeter surveys which currently lack interferometric follow-upobservations, to address science questions which can only be tackled withlarge, statistical samples of SMGs.

A machine-learning method for identifying multi-wavelength counterparts of submillimeter galaxies: training and testing using AS2UDS and ALESS