Gravitational Lensing in the Solar Neighbourhood and Towards the Milky Way Bulge
type: CreativeWork
description: 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: 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.
author: McGill, Peter
language: eng
keywords: gravitational lensing, machine learning, Bayesian statistics, astronomy, microlensing, astrometry, Milky Way
license: https://www.repository.cam.ac.uk/bitstreams/3546116a-2923-4153-92ae-d44c02fb2aad/download
license: https://www.rioxx.net/licenses/all-rights-reserved/
date published: 2022-01-29
date modified: 2026-07-24T01:33:30Z
provider: Cambridge University
publisher: Networked Digital Library of Theses and Dissertations
type: WebPage
source organization: University of Cambridge

PropertyValue
property ID: DOI
value: 10.17863/CAM.77203