The emergence of galaxies in the epoch of reionization
|PhD ceremony:||Mr L. (Laurent) Legrand|
|When:||January 16, 2023|
|Supervisors:||P. (Pratika) Dayal, Prof, prof. dr. L.V.E. (Léon) Koopmans|
|Where:||Academy building RUG|
|Faculty:||Science and Engineering|
The field of high-redshift galaxy observation has seen incredible progress in the last decade and the recent launch of JWST is already providing us with data with unprecedented precision. In order to interpret this flood of data and understand the nature of the first galaxies in our Universe, we need to simulate a large number of galaxies to ensure statistically significant results with a mass resolution high enough to study the properties of low-mass sources and their assembly into massive systems.
I use the ASTRAEUS framework, which couples a start-of-the-art N-body simulation with a galaxy formation semi-analytical model and a semi-numerical reionization scheme. ASTRAEUS includes all the key processes of galaxy evolution such as mergers, accretion, supernova, and reionization feedback. has three key strengths to tackle this challenge, (i) the simulation box is large enough to investigate the role of the environment; (ii) it tracks galaxies in a wide mass range; and (iii) it accounts for the radiative feedback from reionization and allows us to explore different scenario.
Using this simulation, I address the following questions: What is the form of the star formation history of galaxies? What is the respective role of accretion and mergers, both minor and major, in the mass assembly? How is galaxy growth affected by their environment? I also outline a method to further improve the mass resolution of simulations at a low cost, by combining a machine learning algorithm with analytically generated merger trees.