SIMBAD references

2018MNRAS.478.4416R - Mon. Not. R. Astron. Soc., 478, 4416-4432 (2018/August-3)

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


Abstract (from CDS):

The spectrum of a galaxy contains information about its physical properties. Classifying spectra using templates helps to 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 1D self-organizing maps corresponds to an ordering in physical properties, a potentially important feature for the exploration of large data sets.

Abstract Copyright: © 2018 The Author(s) Published by Oxford University Press on behalf of the Royal Astronomical Society

Journal keyword(s): galaxies: high-redshift - galaxies: spectra - methods: observational - methods: statistical - methods: data analysis

Status in Simbad:  waiting for electronic table

Simbad objects: 3

goto Full paper

goto View the reference in ADS

To bookmark this query, right click on this link: simbad:2018MNRAS.478.4416R and select 'bookmark this link' or equivalent in the popup menu


© Université de Strasbourg/CNRS

    • Contact