Hostname: page-component-7bb8b95d7b-wpx69 Total loading time: 0 Render date: 2024-09-19T05:51:13.509Z Has data issue: false hasContentIssue false

An ANN Approach to Classification of Galaxy Spectra for the 2DF Galaxy Redshift Survey

Published online by Cambridge University Press:  25 May 2016

S.R. Folkes
Affiliation:
1Institute of Astronomy, Madingley Road, Cambridge, CB3 0HA, U.K.
O. Lahav
Affiliation:
1Institute of Astronomy, Madingley Road, Cambridge, CB3 0HA, U.K.
S.J. Maddox
Affiliation:
1Institute of Astronomy, Madingley Road, Cambridge, CB3 0HA, U.K.

Extract

Core share and HTML view are not available for this content. However, as you have access to this content, a full PDF is available via the ‘Save PDF’ action button.

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 investigate the technique of Principal Component Analysis when applied to spectra of low S/N. It is found that the projection onto the first 8 Principal Components hold most of the real spectral information, with later projections only adding noise. Using these components as input, we train an Artificial Neural Network (ANN) to classify the noisy simulated spectra into morphological classes. We find that more than 90% of our sample of normal galaxies are correctly classified into one of five morphological classes for simulations at bJ=19.7.

Type
III. Galaxy Formation and Evolution
Copyright
Copyright © Kluwer 1999