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

Ball, N M, Loveday, J, Fukugita, M, Nakamura, O, Okamura, S, Brinkmann, J. and Brunner, R J (2004) Galaxy types in the Sloan Digital Sky Survey using supervised artificial neural networks. Monthly Notices of the Royal Astronomical Society, 348. pp. 1038-1046. ISSN 0035-8711

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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.

Item Type: Article
Additional Information: 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.
Schools and Departments: School of Mathematical and Physical Sciences > Physics and Astronomy
Depositing User: Jonathan Loveday
Date Deposited: 06 Feb 2012 20:11
Last Modified: 14 Mar 2017 00:03
URI: http://sro.sussex.ac.uk/id/eprint/24606

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