Strong gravitational lensing in the era of wide-area sensitive surveys

The next decade will witness a revolution in the use of strong gravitational lenses as sensitive probes to address many open problems in cosmology and extragalactic astrophysics. This is enabled by the forthcoming era of wide-area sensitive imaging surveys. Such surveys are essential for the discovery of the very rare strong gravitational lenses. Despite being first observationally confirmed in 1970s, we only know of ~1000s of lenses. This rarity has meant we cannot fully exploit their promising uses.
This DPhil project is focussed on the discovery of large samples of strong gravitational lenses, of order 100,000s, in surveys carried out by the Vera C. Rubin Observatory, and the Euclid and Nancy A. Roman Space Telescopes. As such, this is a necessary body of work to maximise the discovery of strong gravitational lenses, providing the community with means to discover large samples of high completeness and purity.
The outstanding central problem in strong gravitational lens discovery is the high rate of false positives - among these are e.g., chance alignments of background star forming galaxies with foreground massive ellipticals and high redshift spirals mimicking lensed arcs. To date, supervised machine learning algorithms yield samples that are highly impure (by factors of several). As a result, human visual inspection remains the only means to improve the purity, but it is labour intensive. This project includes an innovative use of discovery algorithms coupled to crowd sourced visual inspection by citizen scientists. This capitalises on the highly successful Zooniverse project Space Warps. As co-founder and -PI of Space Warps, we are uniquely placed to lead this work.
The student will initially run Space Warps assisted discovery systems on existing wide area surveys as pre-cursors to the forthcoming large area surveys. The student will explore the connections between machines and visual inspection. We will focus on expansion on the training samples used for machine learning networks as this is a current limitation in the performance of such networks, along with the construction of active learning loops between the algorithms and the citizen science platform. In addition, we will explore integration of enhanced information derived from the photometry and imaging of the candidates through modelling to rank of the candidates. Ranking is vital in the regime of limited follow-up time for confirmation and cherry picking of sub-samples for science driven analysis.
We will study sub-samples of the sources particularly to e.g., constrain the mass distributions of z~0.5-1.5 galaxies and the properties of high-redshift galaxies at high spatial resolution afforded by strong gravitational lensing.