Abstract:
Modelling the mass distribution of galaxy clusters is pivotal to understand the mass accretion history of the Universe since they are the most massive gravitating objects ever discovered. The Sunyaev-Zel’dovich effect is a tool that identifies clusters of galaxies via their contortion of the cosmic microwave background radiation. Amongst a horde of challenges, foreground and background sources pose a direct obstacle in identifying clusters of galaxies. In this thesis, with the implementation of a Bayesian approach in simulations and observations, we convincingly determine clusters and the source/s that potentially obstruct them, thereby enhancing the strength of cluster detection. This preliminary modelling can be supplemented in detail to probe the astrophysics of galaxy clusters. Another aspect of this project is our analysis of the effect varying noise levels assigned to maps has on finding clusters and sources. We conclude that source detection is objectively more sensitive to a decrease in noise levels than to an increase in the same, while cluster detection improves with lower noise levels and vice versa.
