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

Liu, Ruihan Henry and Hill, Ryley and Scott, Douglas and Almaini, Omar and An, Fangxia and Gubbels, Chris and Hsu, Li-Ting and Lin, Lihwai and Smail, Ian and Stach, Stuart (2019) 'A machine-learning approach for identifying the counterparts of submillimetre galaxies and applications to the GOODS-North field.', Monthly notices of the Royal Astronomical Society., 489 (2). pp. 1770-1786.


Identifying the counterparts of submillimetre (submm) galaxies (SMGs) in multiwavelength images is a critical step towards building accurate models of the evolution of strongly star-forming galaxies in the early Universe. However, obtaining a statistically significant sample of robust associations is very challenging 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 with the Atacama Large Millimeter/submillimeter Array (ALMA), which has provided the resolution necessary for identification in optical and near-infrared images. We use this ALMA sample to develop a training set suitable for machine-learning (ML) algorithms to determine how to identify SMG counterparts in multiwavelength images, using a combination of magnitudes and other derived features. We test several ML algorithms and find that a deep neural network performs the best, accurately identifying 85 per cent of the ALMA-detected optical SMG counterparts in our cross-validation tests. When we carefully tune traditional colour-cut methods, we find that the improvement in using machine learning is modest (about 5 per cent), but importantly it comes at little additional computational cost. We apply our trained neural network to the GOODS-North field, which also has single-dish submm observations from the S2CLS and deep multiwavelength data but little high-resolution interferometric submm imaging, and we find that we are able to classify SMG counterparts for 36/67 of the single-dish submm sources. We discuss future improvements to our ML approach, including combining ML with spectral energy distribution fitting techniques and using longer wavelength data as additional features.

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Publisher statement:This article has been accepted for publication in Monthly Notices of the Royal Astronomical Society ©: 2019 The Royal Astronomical Society. Published by Oxford University Press on behalf of the Royal Astronomical Society. All rights reserved.
Date accepted:31 July 2019
Date deposited:28 October 2019
Date of first online publication:12 August 2019
Date first made open access:28 October 2019

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