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Abstract

Different large-scale structure(LSS) tracers bear rich information about our Universe. In this dissertation, I present my studies on galaxy clusters and multi-tracer cross-correlations to highlight the potential and importance of combining multiple LSS tracers in studying our Universe. I use simulated clusters from the BAHAMAS simulation to study the off-centring effect. I define seven observational-motivated centroids from stars, as well as X-ray and thermal Sunyaev-Zeldovich (tSZ) effect data of these clusters. I find that stacked, mis-centred density profiles yield highly biased shape and size parameters. I also quantify and model the offset distributions between these centroids and the `true' centre of these clusters. The fitting is useful for future measurements of stacked density profiles. With the same set of simulated clusters, I evaluate the ability of Convolutional Neural Networks (CNNs) to measure galaxy cluster masses from cluster images. I independently train four separate networks with images of the four tracers mentioned above. I also train a `multi-channel' CNN that predicts mass from all these four tracers. For the clusters that have masses in the range $10^{13.25}\mathrm{M}_{\odot}