Maresca, J
(2023)
Modern Methods and Their Applications to Strong Gravitational Lensing.
PhD thesis, University of Nottingham.
Abstract
This thesis presents a collection of works, some methodological and others observational, with the common theme of strong gravitational lensing. We have leveraged a wide variety of techniques, from machine learning algorithms to uv-plane modelling, and used a multitude of simulated and real data products to achieve the results contained within this thesis.
In Chapter 2, we explored the feasibility of combining the predictions of an approximate Bayesian neural network with the parametric profile fitting of PyAutoLens. By using the predicted 1-σ uncertainties on the lens model parameters from the CNN to inform our choice of prior within PyAutoLens, we were able to show that a technique that combines both of these tools is able to outperform either method alone. We applied our methodology to a series of increasingly complex and realistic simulated strong gravitational lenses, beginning with simple parametric lenses and sources, and graduating to HUDF sources and lenses extracted from the EAGLE simulation.
In Chapter 3, we addressed the issue of the existence of unphysical source reconstructions by applying a convolutional neural network to detect these solutions, and developed a simple prescription to re-initialise the lens modelling process in a new region of parameter space to help ensure convergence upon the global solution. Such tools are necessary components to developing a truly automated lens modelling pipeline, as will become increasingly necessary in the era of LSST and Euclid.
In Chapter 4, we presented and modelled ∼0.1 arcsec resolution ALMA imaging of seven strong gravitationally lensed galaxies detected by the Herschel Space Observatory. We inferred the mass profiles of the lensing galaxies and by determining the magnification factors, we investigated the intrinsic properties and morphologies of the lensed submillimetre sources. We found that these submillimetre sources all have ratios of star formation rate to dust mass that are consistent with, or in excess of, the mean ratio for high-redshift submillimetre galaxies and low redshift ultra-luminous infrared galaxies. Reconstructions of the background sources reveal that the majority of our sample display disturbed morphologies. The majority of our lens models have mass density slopes close to isothermal, but some systems show significant differences.
In Chapter 5, we present a comparison between performing lens modelling in the uv-plane versus the image-plane. When dealing with interferometer observations, one must make a choice of whether to perform the analysis on the raw visibility data, or to employ an algorithm such as CLEAN to produce an image to work with. When producing an image from visibility data, there are several choices one must make that can impact the final image, such as the adopted lead to biases in the inferred lens model parameters, and whether some choices outperform others. We found that in general, direct modelling of the visibilities provided the most robust means for recovering the lens model parameters, but also that using the Briggs weighting scheme performed better than the natural weighting scheme.
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