Optimizing automatic morphological classification of galaxies with machine learning and deep learning using Dark Energy Survey imaging Article

Cheng, Ting-Yun, Conselice, Christopher J, Aragon-Salamanca, Alfonso et al. (2020). Optimizing automatic morphological classification of galaxies with machine learning and deep learning using Dark Energy Survey imaging . MONTHLY NOTICES OF THE ROYAL ASTRONOMICAL SOCIETY, 493(3), 4209-4228. 10.1093/mnras/staa501

Open Access International Collaboration

cited authors

  • Cheng, Ting-Yun; Conselice, Christopher J; Aragon-Salamanca, Alfonso; Li, Nan; Bluck, Asa FL; Hartley, Will G; Annis, James; Brooks, David; Doel, Peter; Garcia-Bellido, Juan; James, David J; Kuehn, Kyler; Kuropatkin, Nikolay; Smith, Mathew; Sobreira, Flavia; Tarle, Gregory

authors

publication date

  • April 1, 2020

published in

keywords

  • Astronomy & Astrophysics
  • MECHANISM
  • NEOCOGNITRON
  • NEURAL-NETWORK MODEL
  • Physical Sciences
  • SUPPORT VECTOR MACHINES
  • Science & Technology
  • ZOO
  • galaxies: structure
  • methods: data analysis
  • methods: statistical

Digital Object Identifier (DOI)

publisher

  • OXFORD UNIV PRESS

start page

  • 4209

end page

  • 4228

volume

  • 493

issue

  • 3