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

doi:10.1111/j.1365-2966.2004.07429.x

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

^*Corresponding author for this work

Research output: Contribution to journal › Review article › peer-review

111 Citations (Scopus)

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 language	English
Pages (from-to)	1038-1046
Number of pages	9
Journal	Monthly Notices of the Royal Astronomical Society
Volume	348
Issue number	3
DOIs	https://doi.org/10.1111/j.1365-2966.2004.07429.x
Publication status	Published - 2004 Mar 1
Externally published	Yes

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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10.1111/j.1365-2966.2004.07429.x

Cite this

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title = "Galaxy types in the Sloan Digital Sky survey using supervised artificial neural networks",

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.",

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T1 - Galaxy types in the Sloan Digital Sky survey using supervised artificial neural networks

AU - Ball, N. M.

AU - Loveday, J.

AU - Fukugita, M.

AU - Nakamura, O.

AU - Okamura, S.

AU - Brinkmann, J.

AU - Brunner, R. J.

PY - 2004/3/1

Y1 - 2004/3/1

N2 - 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.

AB - 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.

KW - Galaxies: fundamental parameters

KW - Galaxies: photometry

KW - Galaxies: statistics

KW - Methods: data analysis

KW - Methods: statistical

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DO - 10.1111/j.1365-2966.2004.07429.x

M3 - Review article

AN - SCOPUS:1542327730

SN - 0035-8711

VL - 348

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EP - 1046

JO - Monthly Notices of the Royal Astronomical Society

JF - Monthly Notices of the Royal Astronomical Society

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ER -

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

Abstract

Keywords

ASJC Scopus subject areas

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