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A hybrid approach to machine learning annotation of large galaxy image databases

  • Published:2018-10 Issue: Volume:25 Page:257-269
  • ISSN:2213-1337
  • Container-title:Astronomy and Computing
  • language:en
  • Short-container-title:Astronomy and Computing

Author:

Kuminski E.,Shamir L.

Funder

National Science Foundation

Alfred P. Sloan Foundation, United States

Participating Institutions, United States

National Science Foundation, United States

US Department of Energy

National Aeronautics and Space Administration, United States

Japanese Monbukagakusho

Max Planck Society, Germany

Higher Education Funding Council for England

Publisher

Elsevier BV

Subject

Computer Science Applications,Astronomy and Astrophysics,Space and Planetary Science

Reference50 articles.

1. The seventh data release of the Sloan digital sky survey;Abazajian;Astrophys. J. Suppl. Ser.,2009

2. A new approach to galaxy morphology. I. Analysis of the Sloan Digital Sky Survey early data release;Abraham;Astrophys. J.,2003

3. Improving software citation and credit;Allen,2015

4. Practices in code discoverability: Astrophysics source code library;Allen,2012

5. Automatic unsupervised classification of all sloan digital sky survey data release 7 galaxy spectra;Almeida;Astrophys. J.,2010

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