Generate galaxy populations to forecast galaxy surveys
Modeling galaxy formation and evolution presents one of the greatest challenges in modern astrophysics due to its multiscale and multiphysics character. In past decades, tremendous progress has been made toward deep insights into these complex processes, with the help of large amounts of observational data and various physical and empirical models. On one hand, observational data helps to validate or falsify physical models. On the other hand, these models can guide us to devise surveys that can settle down controversies and deepen our understanding in the most effcient way, which is the focus of this talk.
In this talk, I will introduce di erent models for generating galaxy populations, in terms of their physical properties and spatial clustering. To begin with, I will introduce cosmological hydrodynamical simulations, which are the most physical and computationally expensive methods. To reduce the computational cost, alternatives like semi-analytical and empirical models are introduced, which are built on our understanding of galaxy formation and evolution processes and their relation with underlying dark matter. Finally, I will end up with some techniques of creating mock surveys for forecasting future galaxy surveys.