{"id":{"repo_id":"cambridge","oai_identifier":"oai:www.repository.cam.ac.uk:1810/329757"},"canonical_url":"https://search.dev.ndltd.org/etd/cambridge/oai:www.repository.cam.ac.uk:1810/329757","repository":{"repo_id":"cambridge","name":"Cambridge University","base_url":"https://api.repository.cam.ac.uk/server/oai/request"},"display":{"title":"Gravitational Lensing in the Solar Neighbourhood and Towards the Milky Way Bulge","abstract":"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.","abstract_html":"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.","abstract_has_math":false,"creators":["McGill, Peter"],"institution":"University of Cambridge","degree_name":"Doctor of Philosophy (PhD)","degree_level":"Doctoral","degree_discipline":null,"degree_department":null,"school":null,"contributors":[],"advisors":["Evans, Neil Wyn","Belokurov, Vasily"],"committee_chairs":[],"committee_members":[],"year":2022,"date_issued":"2022-01-29","date_published":"2022-01-29","updated_at":"2026-07-24T01:33:30Z","subjects":["gravitational lensing","machine learning","Bayesian statistics","astronomy","microlensing","astrometry","Milky Way"],"languages":["eng"],"rights":[],"rights_urls":["https://www.repository.cam.ac.uk/bitstreams/3546116a-2923-4153-92ae-d44c02fb2aad/download","https://www.rioxx.net/licenses/all-rights-reserved/"],"identifier_entries":[]},"links":{"outbound_url":"https://doi.org/10.17863/CAM.77203","outbound_label":"DOI","outbound_source":"dc:identifier.doi"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Evans, Neil Wyn","Belokurov, Vasily"]},{"key":"dc:contributor.sponsor","label":"Sponsor","values":["PhD studentship from the Sciences and Technologies Research Council (STFC)"]},{"key":"dc:creator","label":"Author","values":["McGill, Peter"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.issued","label":"Date","values":["2022-01-29"]},{"key":"dc:publisher.institution","label":"Dc Publisher Institution","values":["University of Cambridge"]},{"key":"dc:relation.isreferencedby.uri","label":"Dc Relation Isreferencedby URI","values":["https://www.repository.cam.ac.uk/handle/1810/329757"]},{"key":"dc:type","label":"Dc Type","values":["Thesis"]},{"key":"dc:type.qualificationlevel","label":"Dc Type Qualificationlevel","values":["Doctoral"]},{"key":"dc:type.qualificationname","label":"Dc Type Qualificationname","values":["Doctor of Philosophy (PhD)"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["gravitational lensing","machine learning","Bayesian statistics","astronomy","microlensing","astrometry","Milky Way"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["eng"]},{"key":"dc:rights","label":"Dc Rights","values":["https://www.repository.cam.ac.uk/bitstreams/3546116a-2923-4153-92ae-d44c02fb2aad/download","https://www.rioxx.net/licenses/all-rights-reserved/"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.doi","label":"DOI","values":["10.17863/CAM.77203"]},{"key":"dc:identifier.uri","label":"Identifier URI","values":["https://www.repository.cam.ac.uk/bitstreams/47fa5691-a2a5-4952-9114-b4b7892450f8/download","https://www.repository.cam.ac.uk/bitstreams/383f6900-5e4a-480f-a5cc-9cce9e9c5fb5/download"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["This thesis is concerned with looking for and characterizing microlensing events in two places that are relatively unexplored. 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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. 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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. 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