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

*この研究の対応する著者

研究成果: Review article査読

103 被引用数 (Scopus)

抄録

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.

本文言語English
ページ(範囲)1038-1046
ページ数9
ジャーナルMonthly Notices of the Royal Astronomical Society
348
3
DOI
出版ステータスPublished - 2004 3 1
外部発表はい

ASJC Scopus subject areas

  • 天文学と天体物理学
  • 宇宙惑星科学

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