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UBC Theses and Dissertations

Probing the universe with multiple large-scale structure tracers Yan, Ziang


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 $10^{13.25}\mathrm{M}_{\odot}<M_{200}<10^{14.5}\mathrm{M}_{\odot}$, all of the five networks give mean fractional mass biases of order 1\% with scatters below $\lesssim$20\%, which outperforms traditional scaling relation methods. I also attempt to interpret the neural networks and find that they can capture the shape and substructure of galaxy clusters. I constrain the redshift dependence of the thermodynamics of intergalactic gas by cross-correlating galaxy positions from the fourth data release of the Kilo-Degree Survey (KiDS) and the tSZ $y$ map released by the \planck collaboration. To constrain galaxy bias, I cross-correlate galaxies with CMB lensing or galaxy lensing. Both give consistent results. My constraints on gas pressure bias agree with previous studies. I also make a forecast for future cosmological surveys. I also evaluate the potential contamination in the \planck $y$ map coming from cosmic infrared background and Galactic dust by comparing the $y$-lensing cross-correlation with a clean $y$ map and a contaminated $y$ map. I find that the contamination in the $y$ map does not significantly affect the $y$-galaxy lensing cross-correlation but might bias the $y$-CMB lensing cross-correlation.

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