Details

Autor(en) / Beteiligte

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

Titel

A probabilistic approach to emission-line galaxy classification

Ist Teil von

• Monthly notices of the Royal Astronomical Society, 2017-12, Vol.472 (3), p.2808-2822

Ort / Verlag

London: Oxford University Press

Erscheinungsjahr

2017

Quelle

Alma/SFX Local Collection

Beschreibungen/Notizen

• Abstract We invoke 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 α versus [N ii]/H α (WHAN) diagrams, using spectroscopic data from the Sloan Digital Sky Survey Data Release 7 and SEAGal/STARLIGHT data sets. We apply a GMM to empirically define classes of galaxies in a three-dimensional space spanned by the log  [O iii]/H β, log  [N ii]/H α and log  EW(H α) optical parameters. The best-fitting GMM based on several statistical criteria suggests a solution around 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 galactic 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 four GCs – for the existence of a Seyfert/low-ionization nuclear emission-line region (LINER) dichotomy in our sample. Notwithstanding, the inclusion of an additional GC5 unravels it. The GC5 appears associated with the LINER and passive galaxies on the BPT and WHAN diagrams, respectively. This indicates that if the Seyfert/LINER dichotomy is there, it does not account significantly to the global data variance and may be overlooked by standard metrics of goodness of fit. Subtleties aside, we demonstrate the potential of our methodology to recover/unravel different objects inside the wilderness of astronomical data sets, without lacking the ability to convey physically interpretable results. The probabilistic classifications from the GMM analysis are publicly available within the COINtoolbox at https://cointoolbox.github.io/GMM_Catalogue/.