MNRAS-2004-Ball-1038-46.pdf (3.15 MB)
Galaxy types in the Sloan Digital Sky Survey using supervised artificial neural networks
journal contribution
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 BrunnerSupervised 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 SocietyISSN
0035-8711Publisher
Wiley-BlackwellExternal DOI
Volume
348Page range
1038-1046Department 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
2012-02-06First Open Access (FOA) Date
2016-03-22First Compliant Deposit (FCD) Date
2016-11-15Usage metrics
Categories
No categories selectedKeywords
Licence
Exports
RefWorks
BibTeX
Ref. manager
Endnote
DataCite
NLM
DC