Galaxy types in the Sloan Digital Sky survey using supervised artificial neural networks

N. M. Ball, J. Loveday, M. Fukugita, O. Nakamura, S. Okamura, J. Brinkmann, R. J. Brunner

Research output: Contribution to journalReview article

Abstract

Supervised artificial neural networks are used to predict useful properties of galaxies in the Sloan Digital Sky Survey, in this instance morphological classifications, spectral types and redshifts. By giving the trained networks unseen data, it is found that correlations between predicted and actual properties are around 0.9 with rms errors of order ten per cent. Thus, given a representative training set, these properties may be reliably estimated for galaxies in the survey for which there are no spectra and without human intervention.

Original languageEnglish (US)
Pages (from-to)1038-1046
Number of pages9
JournalMonthly Notices of the Royal Astronomical Society
Volume348
Issue number3
DOIs
StatePublished - Mar 1 2004

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Keywords

  • Galaxies: fundamental parameters
  • Galaxies: photometry
  • Galaxies: statistics
  • Methods: data analysis
  • Methods: statistical

ASJC Scopus subject areas

  • Astronomy and Astrophysics
  • Space and Planetary Science

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