A machine-learning method for identifying multi-wavelength counterparts
  of submillimeter galaxies: training and testing using AS2UDS and ALESS
  We describe the application of the supervised machine-learning algorithms to
identify the likely multi-wavelength counterparts to submillimeter sources
detected in panoramic, single-dish submillimeter surveys. As a training set, we
employ 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-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 $\sim$85 percent of ALMA-identified SMGs which
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 $\sim$100 ALMA-identified SMGs
from the ALMA/LABOCA ECDF-South Survey (ALESS). To further test our
methodology, we stack the 870$\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 $S_{\rm 870\mu m}=(0.61\pm0.03)$ 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 which currently lack interferometric follow-up
observations, to address science questions which can only be tackled with
large, statistical samples of SMGs.
