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Abstract

Statistical methods play a crucial role in modern astronomical research. The development and understanding of these methods will be of fundamental importance to future work on large astronomical surveys. In this thesis I showcase three different statistical approaches to survey data. I first apply a semi-supervised dimensionality reduction technique to cluster similar high resolution spectra from the GALAH survey to identify 54 candidate extremely metal-poor stars. The approach shows promising potential for implementation in future large-scale stellar spectroscopic surveys. Next, I employ a method to classify sources in the Gaia survey as stars, galaxies or quasars, making use of additional infrared photometry from CatWISE2020 and discussing the importance of applying adjusted priors to probabilistic classification. Lastly, I utilise a method to estimate the rotational parameters of star clusters in Gaia, with an application to open clusters. This is done by considering the rotation of a cluster as a 3D solid body, and finding the best fitting parameters by sampling constructed likelihood functions. The methods developed in this thesis underscore the significant contributions statistical methodologies make to astronomy, and illustrate how the development and application of statistical methods will be essential for extracting meaningful insights from future large scale astronomical surveys.