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

TL;DR

This paper explores using self-organizing maps to classify galaxy spectra in the redshift range 0.5 to 1, demonstrating their effectiveness and physical relevance compared to other methods.

Contribution

It introduces a semi-supervised self-organizing map approach for galaxy spectral classification and compares its performance with other clustering methods.

Findings

Self-organizing maps classify galaxy spectra with high consistency.

Class ordering correlates with physical galaxy properties.

Method outperforms chi-squared minimization and supervised neural networks.

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.