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.
This is an author-created, un-copyedited version of an article accepted for publication/published in Astrophysical Journal. IOP Publishing Ltd is not responsible for any errors or omissions in this version of the manuscript or any version derived from it. The Version of Record is available online at doi:10.3847/1538-4357/aacdaa