# Galaxy classification: A machine learning analysis of GAMA catalogue data

## TL;DR

This study applies machine learning techniques to classify galaxies using multiple GAMA catalogues, revealing limitations of visual classification and identifying key features that distinguish certain galaxy types.

## Contribution

It introduces a comprehensive machine learning analysis across five galaxy catalogues, highlighting the challenges of visual-based classification schemes.

## Key findings

- Only Little Blue Spheroids are consistently separable.
- Most galaxy classes are not well distinguished by the features.
- Discriminative parameters for galaxy classes are identified.

## 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 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 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.