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

Liu, Ruihan Henry; Hill, Ryley; Scott, Douglas; Almaini, Omar; An, Fangxia; Gubbels, Chris; Hsu, Li Ting; Lin, Lihwai; Smail, Ian; Stach, Stuart

Abstract

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.

Citation

Liu, R. H., Hill, R., Scott, D., Almaini, O., An, F., Gubbels, C., …Stach, S. (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), 1770-1786. https://doi.org/10.1093/mnras/stz2228

• Journal Article Type: Article
• Acceptance Date: Jul 31, 2019
• Online Publication Date: Aug 12, 2019
• Publication Date: 2019-10
• Deposit Date: Jan 29, 2020
• Publicly Available Date: Jan 29, 2020
• Journal: Monthly Notices of the Royal Astronomical Society
• Print ISSN: 0035-8711
• Electronic ISSN: 1365-2966
• Publisher: Oxford University Press
• Peer Reviewed: Peer Reviewed
• Volume: 489
• Issue: 2
• Pages: 1770-1786
• DOI: https://doi.org/10.1093/mnras/stz2228
• Keywords: Methods: data analysis – Galaxies: starburst – Submillimetre: galaxies
• Public URL: https://nottingham-repository.worktribe.com/output/2470848
• Publisher URL: https://academic.oup.com/mnras/article/489/2/1770/5548789
• Additional Information: This article has been accepted for publication in Monthly Notices of the Royal Astronomical Society ©: 2019 The authors Published by Oxford University Press on behalf of the Royal Astronomical Society. All rights reserved.