Galaxy And Mass Assembly (GAMA): Self-Organizing Map Application on
  Nearby Galaxies
  Galaxy populations show bimodality in a variety of properties: stellar mass,
colour, specific star-formation rate, size, and S\'ersic 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 2 and 6 clusters.
  Here we use Self Organizing Maps (SOM), an unsupervised learning technique
which can be used to visualize similarity in a higher dimensional space using a
2D representation, to map these five-dimensional clusters in the feature space
onto two-dimensional projections. To further analyze these clusters, using the
SOM information, we agree with previous results that the sub-populations found
in the feature space can be reasonably mapped onto three or five clusters. We
explore where the "green valley" galaxies are mapped onto 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 onto 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.
