Galaxy and mass assembly (GAMA): Self-Organizing Map application on nearby galaxies

Abstract

ABSTRACT Galaxy populations show bimodality in a variety of properties: stellar mass, colour, specific star-formation rate, size, and Sérsic index. These parameters are our feature space. We use an existing sample of 7556 galaxies from the Galaxy and Mass Assembly (GAMA) survey, represented using five features and the K-means clustering technique, showed that the bimodalities are the manifestation of a more complex population structure, represented by between two and six clusters. Here we use Self-Organizing Maps (SOM), an unsupervised learning technique that can be used to visualize similarity in a higher dimensional space using a 2D representation, to map these 5D clusters in the feature space on to 2D projections. To further analyse these clusters, using the SOM information, we agree with previous results that the sub-populations found in the feature space can be reasonably mapped on to three or five clusters. We explore where the ‘green valley’ galaxies are mapped on to the SOM, indicating multiple interstitial populations within the green valley population. Finally, we use the projection of the SOM to verify whether morphological information provided by GalaxyZoo users, for example, if features are visible, can be mapped on to the SOM-generated map. Voting on whether galaxies are smooth, likely ellipticals, or ‘featured’ can reasonably be separated but smaller morphological features (bar, spiral arms) can not. SOMs promise to be a useful tool to map and identify instructive sub-populations in multidimensional galaxy survey feature space, provided they are large enough.