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