Exploring the High-Redshift Universe with SKA and Next-Generation Observatories

Abstract

The formation of the first stars in the Universe remains a key challenge in cosmology, requiring a multi-wavelength observational strategy across a range of facilities. Telescopes like the James Webb Space Telescope (JWST) enable direct observation of early galaxies, while upcoming radio telescopes such as the SKA will capture the hydrogen signal from the early Universe. Interpreting the extensive datasets from these facilities demands sophisticated theoretical models that balance detailed physics with computational efficiency, supported by advanced statistical methods to probe the parameter space. In this talk, we will discuss recent advancements in modelling the high-redshift Universe, along with the evolution of machine learning-driven statistical techniques. Together, these developments are essential for bridging the gap between theoretical predictions and observations, enabling a deeper understanding of cosmic evolution with next-generation observational capabilities.