Automated Simulations of Galaxy Morphology Evolution using Deep Learning
  and Particle Swarm Optimisation
  The formation of Hoag-type galaxies with central spheroidal galaxies and
outer stellar rings has yet to be understood in astronomy. We consider that
these unique objects were formed from the past interaction between elliptical
galaxies and gas-rich dwarf galaxies. We have modelled this potential formation
process through simulation. These numerical simulations are a means of
investigating this formation hypothesis, however the parameter space to be
explored for these simulations is vast. Through the application of machine
learning and computational science, we implement a new two-fold method to find
the best model parameters for stellar rings in the simulations. First, test
particle simulations are run to find a possible range of parameters for which
stellar rings can be formed around elliptical galaxies (i.e. Hoag-type
galaxies). A novel combination of particle swarm optimisation and Siamese
neural networks has been implemented to perform the search over the parameter
space and test the level of consistency between observations and simulations
for numerous models. Upon the success of this initial step, we subsequently run
full chemodynamical simulations for the derived range of model parameters in
order to verify the output of the test particle simulations. We successfully
find parameter sets at which stellar rings can be formed from the interaction
between a gas-rich dwarf galaxy and a central elliptical galaxy. This is
evidence that supports our hypothesis about the formation process of Hoag-type
galaxies. In addition, this suggests that our new two-fold method has been
successfully implemented in this problem search-space and can be investigated
further in future applications. ~
