Galaxy classification: A machine learning analysis of GAMA catalogue data

@article{Nolte2019GalaxyCA,
  title={Galaxy classification: A machine learning analysis of GAMA catalogue data},
  author={Aleke Nolte and Lingyu Wang and Maciej Bilicki and Benne Holwerda and Michael Biehl},
  journal={ArXiv},
  year={2019},
  volume={abs/1903.07749}
}
  • Aleke Nolte, Lingyu Wang, +2 authors Michael Biehl
  • Published in Neurocomputing 2019
  • Mathematics, Physics, Computer Science
  • Abstract 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… CONTINUE READING

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