Semi-analytical (SA) galaxy formation models have wide applications, and they are complementary to hydrodynamical simulations, which are more physically detailed but also much more computationally expensive. It is important to make semi-analytical models as physical as possible for the robustness of their applications. In this work we try to improve the modelling of two important processes, supernova (SN) feedback and gas cooling, in the SA model galform. We first improve the SN feedback recipe in a phenomenological way, using the constraints from four observations, including the Milky Way (MW) satellite galaxy luminosity function, the faint end of the field galaxy luminosity function, the redshift at which the universe was half reionized and the stellar metallicity of the MW satellites. We find that these observations favour a SN feedback model in which the feedback strength evolves with redshift. We further apply this improved model to investigate some details of reionization. We then develop a new, more physical model for gas cooling in halos in semi-analytical models. We compare this new cooling model with a cosmological hydrodynamical simulation with stripped-down galaxy formation physics running with the grid-based moving mesh code arepo, along with two previous models (GFC1 and GFC2) in galform and the models in l-galaxies and morgana. We find that generally all SA models predict cumulative cool masses close to the simulation, but the mass cooling rates in low redshift massive halos are overestimated. These SA models overpredict the specific angular momenta of the cool gas for low mass halos, while for low redshift massive halos, the predictions from the new cooling model generally agree better with the simulation than the earlier SA cooling models. We also use the simulation to investigate gas cooling in individual halos in more detail.
| Identifer | oai:union.ndltd.org:bl.uk/oai:ethos.bl.uk:721027 |
| Date | January 2017 |
| Creators | Hou, Jun |
| Publisher | Durham University |
| Source Sets | Ethos UK |
| Detected Language | English |
| Type | Electronic Thesis or Dissertation |
| Source | http://etheses.dur.ac.uk/12259/ |
Page generated in 0.0144 seconds