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Gravitational Lensing in the Solar Neighbourhood and Towards the Milky Way Bulge 10.17863/CAM.77203 McGill, Peter Evans, Neil Wyn Belokurov, Vasily 0000000200389584 This thesis is concerned with looking for and characterizing microlensing events in two places that are relatively unexplored. Leveraging astrometry from the Gaia satellite, I search for predicted close stellar alignments by lenses in the solar neighbourhood that will give rise to a microlensing event. Using Near-Infrared (NIR) photometry from the Vista Variables in the Via Lactea (VVV) survey, I extract microlensing events towards highly-extinct regions of the Galactic bulge. In both cases, I develop a Bayesian methodology to characterize the microlensing signals. In Chapter 1, I review the history of finding microlensing events both by predicting stellar alignments and by monitoring millions of stars. I describe two uses for these types of events; lens mass determination and probing structure of the Galactic bulge. In Chapter 2, I detail the microlensing signals and the methods which underpin the results presented in the rest of the thesis. In Chapter 3, I find a predicted microlensing event where the lens is a nearby white dwarf. Analysis of this event permitted a direct mass determination of the white dwarf which in turn allowed a test of the white dwarf mass-radius relationship. In Chapter 4, I present a search for predicted photometric microlensing events. For these events, I investigate combining prior astrometric information from Gaia with photometric follow-up data to extract the lens mass. In Chapter 5, I extend predicted microlensing searches using Gaia in combination with astrometry from the VVV. In Chapter 6, I critically examine the reliability of predicted microlensing events found with Gaia. I find that the majority of high-quality events expected to occur over Gaia’s life time are in fact spurious. Finally, in Chapter 7, I use machine learning to extract 1959 microlensing events from the VVV and I develop a Bayesian methodology to characterize their sparsely sampled signals. University of Cambridge 2022-01-29 Thesis Doctoral Doctor of Philosophy (PhD) eng PhD studentship from the Sciences and Technologies Research Council (STFC) https://www.repository.cam.ac.uk/handle/1810/329757 https://www.repository.cam.ac.uk/bitstreams/47fa5691-a2a5-4952-9114-b4b7892450f8/download 56367452932bb6b3c19e03e2d5be7c9d https://www.repository.cam.ac.uk/bitstreams/383f6900-5e4a-480f-a5cc-9cce9e9c5fb5/download baaf736f9299fcf6365a83e97ee3115b https://www.repository.cam.ac.uk/bitstreams/3546116a-2923-4153-92ae-d44c02fb2aad/download 353adac0d1ebdfd65ab16480263c3c87 https://www.rioxx.net/licenses/all-rights-reserved/ gravitational lensing machine learning Bayesian statistics astronomy microlensing astrometry Milky Way