Galaxy classification: A machine learning analysis of GAMA catalogue data

Aleke Nolte*, Lingyu Wang, Maciej Bilicki, Benne Holwerda, Michael Biehl

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

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We present a machine learning analysis of five labelled galaxy catalogues from the Galaxy And Mass Assembly (GAMA): The SersicCatVIKING and SersicCatUKIDSS catalogues containing morphological features, the GaussFitSimplecatalogue containing spectroscopic features, the MagPhys catalogue including physical parameters for galaxies, and the Lambdar catalogue, which contains photometric measurements. Extending work previously presented at the ESANN 2018 conference – in an analysis based on Generalized Relevance Matrix Learning Vector Quantization and Random Forests – we find that neither the data from the individual catalogues nor a combined dataset based on all 5 catalogues fully supports the visual-inspection-based galaxy classification scheme employed to categorise the galaxies. In particular, only one class, the Little Blue Spheroids, is consistently separable from the other classes. To aid further insight into the nature of the employed visual-based classification scheme with respect to physical and morphological features, we present the galaxy parameters that are discriminative for the achieved class distinctions.
Original languageEnglish
Pages (from-to)172-190
Number of pages19
Issue numberSI
Early online date3-Feb-2019
Publication statusPublished - 21-May-2019


  • Learning Vector Quantization
  • Relevance learning
  • Galaxy classification
  • Random Forests

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