Classifying galaxy spectra at 0.5<z<1 with self-organizing maps
  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.
