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Title:
An ANN Approach to Classification of Galaxy Spectra for the 2DF Galaxy Redshift Survey
Authors:
Folkes, S. R.; Lahav, O.; Maddox, S. J.
Affiliation:
AA(Institute of Astronomy, Cambridge, UK, ), AB(Institute of Astronomy, Cambridge, UK, ), AC(Institute of Astronomy, Cambridge, UK, )
Publication:
Cosmological Parameters and the Evolution of the Universe. Edited by Katsuhiko Sato. Publisher: Dordrecht, Boston: Kluwer Academic, 1999. ("Proceedings of the 183rd symposium of the International Astronomical Union held in Kyoto, Japan, August 18-22, 1997"., p. 154
Publication Date:
00/1999
Origin:
ADS; AUTHOR ttp://ads.harvard.edu
Bibliographic Code:
1999IAUS..183..154F

Abstract

We present a method for automated classification of galaxies with low signal-to-noise (S/N) spectra typical of redshift surveys. We develop spectral simulations based on the parameters for the 2dF Galaxy Redshift Survey, and with these simulations we investigate the technique of Principal Component Analysis when applied specifically to spectra of low S/N. We relate the objective principal components to features in the spectra and use a small number of components to successfully reconstruct the underlying signal from the low quality spectra. Using the principal components as input, we train an Artificial Neural Network (ANN) to classify the noisy simulated spectra into morphological classes, revealing the success of the classification against the observed bJ magnitude of the source, which we compare with alternative methods of classification. We find that more than 90% of our sample of normal galaxies are correctly classified into one of five broad morphological classes for simulations at bJ = 19.7. We also show the application of these methods to spectra from other sources.

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