1. Classifying galaxy spectra at 0.5
- Author
-
Rahmani, S., Teimoorinia, H., and Barmby, P.
- Subjects
Astrophysics of Galaxies (astro-ph.GA) ,FOS: Physical sciences ,Astrophysics::Cosmology and Extragalactic Astrophysics ,Astrophysics::Galaxy Astrophysics - Abstract
The spectrum of a galaxy contains information about its physical properties. Classifying spectra using templates helps elucidate the nature of a galaxy's energy sources. In this paper, we investigate the use of self-organizing maps in classifying galaxy spectra against templates. We trained semi-supervised self-organizing map networks using a set of templates covering the wavelength range from far ultraviolet to near infrared. The trained networks were used to classify the spectra of a sample of 142 galaxies with 0.5 < z < 1 and the results compared to classifications performed using K-means clustering, a supervised neural network, and chi-squared minimization. Spectra corresponding to quiescent galaxies were more likely to be classified similarly by all methods while starburst spectra showed more variability. Compared to classification using chi-squared minimization or the supervised neural network, the galaxies classed together by the self-organizing map had more similar spectra. The class ordering provided by the one-dimensional self-organizing maps corresponds to an ordering in physical properties, a potentially important feature for the exploration of large datasets., MNRAS in press; 19 pages, 22 figures
- Published
- 2018
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