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 |
| Publication status | Published - 2004 Mar 1 |
| Externally published | Yes |
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Keywords
- Galaxies: fundamental parameters
- Galaxies: photometry
- Galaxies: statistics
- Methods: data analysis
- Methods: statistical
ASJC Scopus subject areas
- Space and Planetary Science
Cite this
Ball, N. M., Loveday, J., Fukugita, M., Nakamura, O., Okamura, S., Brinkmann, J., & 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(3), 1038-1046.