http://arxiv.org/abs/1901.09594
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. Recent follow-up observations of a large sample of single dish-detected SMGs in the UKIDSS-UDS field with the Atacama Large Millimeter/submillimeter Array (ALMA) have provided the resolution necessary for multiwavelength identification from optical to infrared wavelengths. 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 a number of 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 crossvalidation 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 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.
R. Liu, R. Hill, D. Scott, et. al.
Tue, 29 Jan 19
30/62
Comments: 18 pages, 5 figures, submitted to MNRAS
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