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

Understand

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.