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Identifying the counterparts of submillimetre (submm) galaxies (SMGs) inmultiwavelength images is a critical step towards building accurate models ofthe evolution of strongly star-forming galaxies in the early Universe. However,obtaining a statistically significant sample of robust associations is verychallenging due to the poor angular resolution of single-dish submm facilities.Recently, a large sample of single-dish-detected SMGs in the UKIDSS UDS field,a subset of the SCUBA-2 Cosmology Legacy Survey (S2CLS), was followed up withthe Atacama Large Millimeter/submillimeter Array (ALMA), which has provided theresolution necessary for identification in optical and near-infrared images. Weuse this ALMA sample to develop a training set suitable for machine-learning(ML) algorithms to determine how to identify SMG counterparts inmultiwavelength images, using a combination of magnitudes and other derivedfeatures. We test several ML algorithms and find that a deep neural networkperforms the best, accurately identifying 85 per cent of the ALMA-detectedoptical SMG counterparts in our cross-validation tests. When we carefully tunetraditional colour-cut methods, we find that the improvement in using machinelearning is modest (about 5 per cent), but importantly it comes at littleadditional computational cost. We apply our trained neural network to theGOODS-North field, which also has single-dish submm observations from the S2CLSand deep multiwavelength data but little high-resolution interferometric submmimaging, and we find that we are able to classify SMG counterparts for 36/67 ofthe single-dish submm sources. We discuss future improvements to our MLapproach, including combining ML with spectral energy distribution-fittingtechniques and using longer wavelength data as additional features.

A machine learning approach for identifying the counterparts of submillimetre galaxies and applications to the GOODS-North field