Optimising Semi-Analytic Models of Galaxy Formation

Semi-analytic models (SAMs) are one of the most effective tools for exploring the physics of galaxy evolution. The computational efficiency of SAMs make them an ideal candidate to use to explore the multi-dimensional parameter space of galaxy formation models, in combination with Bayesian techniques. This work utilises Particle Swarm Optimisation on SHARK, an open-source SAM, to compare the results of the models to a set of observables and identify the best-fit parameter set.