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

@article{Liu2019AMA,
  title={A machine-learning approach for identifying the counterparts of submillimetre galaxies and applications to the GOODS-North field},
  author={R. H. Liu and Ryley Hill and Douglas Scott and Omar Almaini and Fangxia An and Christopher Gubbels and L. T. Hsu and Lihwai Lin and I. R. Smail and Stuart M. Stach},
  journal={Monthly Notices of the Royal Astronomical Society},
  year={2019}
}
  • R. Liu, R. Hill, S. Stach
  • Published 28 January 2019
  • Physics
  • Monthly Notices of the Royal Astronomical Society
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… 

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