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A probabilistic approach to emission-line galaxy classification

Abstract · Mar 22, 2017 11:48 ·

astro-ph-ga astro-ph-im stat-ml

Arxiv Abstract

  • R. S. de Souza
  • M. L. L. Dantas
  • M. V. Costa-Duarte
  • E. D. Feigelson
  • M. Killedar
  • P. -Y. Lablanche
  • R. Vilalta
  • A. Krone-Martins
  • R. Beck
  • F. Gieseke

This work employs a Gaussian mixture model (GMM) to jointly analyse two traditional emission-line classification schemes of galaxy ionization sources: the Baldwin-Phillips-Terlevich (BPT) and W$_{H\alpha}$ vs. [NII]/H$\alpha$ (WHAN) diagrams, using spectroscopic data from the Sloan Digital Sky Survey Data Release 7 and SEAGal/STARLIGHT datasets. We apply a GMM to empirically define classes of galaxies in a three-dimensional space spanned by the log [OIII]/H\beta, log [NII]/H\alpha, and log EW(H{\alpha}) optical parameters. The best-fit GMM based on several statistical criteria consists of four Gaussian components (GCs), which are capable to explain up to 97 per cent of the data variance. Using elements of information theory, we compare each GC to their respective astronomical counterpart. GC1 and GC4 are associated with star-forming galaxies, suggesting the need to define a new starburst subgroup. GC2 is associated with BPT’s Active Galaxy Nuclei (AGN) class and WHAN’s weak AGN class. GC3 is associated with BPT’s composite class and WHAN’s strong AGN class. Conversely, there is no statistical evidence – based on GMMs – for the existence of a Seyfert/LINER dichotomy in our sample. We demonstrate the potential of our methodology to recover/unravel different objects inside the wilderness of astronomical datasets, without lacking the ability to convey physically interpretable results; hence being a precious tool for maximum exploitation of the ever-increasing astronomical surveys. The probabilistic classifications from the GMM analysis are publicly available within the COINtoolbox (

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