Automated Simulations of Galaxy Morphology Evolution using Deep Learning and Particle Swarm Optimisation
Eleanor Leung, Kenji Bekki, and Lyndon While

TL;DR
This paper introduces a novel two-fold machine learning approach combining particle swarm optimisation and neural networks to efficiently explore simulation parameters for galaxy formation, specifically Hoag-type galaxies, supporting the interaction hypothesis.
Contribution
The paper presents a new combined method using particle swarm optimisation and Siamese neural networks to identify parameters for galaxy formation simulations, advancing computational astrophysics.
Findings
Successfully identified parameters for stellar ring formation
Supported the hypothesis of galaxy interaction origin
Demonstrated effectiveness of the two-fold method
Abstract
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…
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Taxonomy
TopicsGalaxies: Formation, Evolution, Phenomena · Computational Physics and Python Applications · Metaheuristic Optimization Algorithms Research
