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

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posted on 2023-06-08, 05:22 authored by N M Ball, Jonathan LovedayJonathan Loveday, M Fukugita, O Nakamura, S Okamura, J. Brinkmann, R J Brunner
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.

History

Publication status

  • Published

File Version

  • Published version

Journal

Monthly Notices of the Royal Astronomical Society

ISSN

0035-8711

Publisher

Wiley-Blackwell

Volume

348

Page range

1038-1046

Department affiliated with

  • Physics and Astronomy Publications

Notes

Additional authors: Okamura S, Brinkmann J, Brunner R J. This paper demonstrates that supervised artificial neural networks are able to reliably predict Hubble type, spectral type and redshift from standard SDSS galaxy imaging parameters. First author was Loveday's student. Fukugita et al provided training set.

Full text available

  • Yes

Peer reviewed?

  • Yes

Legacy Posted Date

2012-02-06

First Open Access (FOA) Date

2016-03-22

First Compliant Deposit (FCD) Date

2016-11-15

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