Journal
NEUROCOMPUTING
Volume 342, Issue -, Pages 172-190Publisher
ELSEVIER
DOI: 10.1016/j.neucom.2018.12.076
Keywords
Learning Vector Quantization; Relevance learning; Galaxy classification; Random Forests
Categories
Funding
- STFC (UK)
- ARC (Australia)
- AAO
- EUs Horizon 2020 research and innovation programme under Marie Sklodowska-Curie grant [721463]
- Netherlands Organisation for Scientific Research, NWO [614.001.451]
- Polish Ministry of Science and Higher Education [DIR/WK/2018/12]
- Marie Curie Actions (MSCA) [721463] Funding Source: Marie Curie Actions (MSCA)
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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 GaussFitSimple catalogue 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 visualinspection-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. (C) 2019 Elsevier B.V. All rights reserved.
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