Simulating the formation of the first galaxies

This project will build on work within the Dark-ages, Reionization And Galaxy-formation Observables Numerical Simulation project (DRAGONS).

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This project is open for Bachelor, Honours and PhD students.
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This project will build on work within the Dark-ages, Reionisation And 
Galaxy-formation Observables Numerical Simulation project (DRAGONS) 
which combines semi-analytic modeling for galaxy formation designed to 
accurately represent the growth of galaxies, with a semi-numerical model 
for the growth and evolution of ionised structure. The goal of DRAGONS 
is self-consistent modelling of observations of high redshift galaxies 
and the structure and morphology of reionisation.

The James Webb Space Telescope (JWST) has revolutionised our view of the 
formation of galaxies at the highest redshifts. In the near future, the 
Square Kilometer Array (SKA) will enable direct mapping of the 
intergalactic medium (IGM) through 21-cm hydrogen emission lines, which 
encode critical information about the faintest galaxies. Understanding 
how mass buids up in these galaxies and how the stars formed interact 
with the intergalactic and interstellar media requires detailed 
simulations.

Specific research projects include:

  1. Investigating how emission lines can be used to accurately determine 
    star formation rates, metallicities, and the escape of ionising 
    radiation that reionises the IGM.
  2. Exploring the origins of massive quiescent galaxies and understanding 
    how these can have formed at such high redshifts.
  3. Analysing how upcoming IGM observations with the SKA will reveal the 
    properties of faint galaxies.
  4. Assessing the impact of active galactic nuclei (AGN) on the 
    ionisation and heating histories of the IGM.
  5. Constraining reionisation using Lyman-alpha absorption features, 
    including the occurrence of Lyman-alpha emitters and the large 
    fluctuations seen in Lyman-alpha forests.
  6. Forward-modeling observables of the very first galaxies that host 
    Population III stars in minihalos.
  7. Applying machine learning algorithms to enhance cosmological 
    simulations and improve inference studies.

Members

Supervisor

Yuxiang Qin

ARC DECRA Fellow

Professor Stuart Wyithe

Director, Research School of Astronomy and Astrophysics