The goal of my thesis is to model the merging timescale of galaxies in N-body simulations and provide an analytical formula that can be applied in galaxy formation models. I assessed the algorithms used to identify and track simulated mergers using a new visualisation tool, the dendogram. I then quantified how merger timescales depend on galaxy orbits and compared to predictions from prescriptions in the literature. Finally, I used these insights to develop a new model for the merger timescale, showing how it improves on existing prescriptions, and explored its implications for predictions in the semi-analytical galaxy formation model, Shark.
|Qualification||Doctor of Philosophy|
|Award date||2 Aug 2020|
|Publication status||Unpublished - 2020|