Supervisory team: Sugata Kaviraj, Aaron Watkins
Our current understanding of the Universe is dominated by bright objects (e.g. massive galaxies like the Milky Way), because such systems are brighter than the detection thresholds of past large observational surveys (e.g. the SDSS). However, the majority of stars in the Universe actually reside in the faint or ‘low surface brightness’ regime, i.e. in objects and structures that are much fainter than the detection limits of past surveys. This regime contains all dwarf (low-mass) galaxies which dominate the galaxy number density, making them critical to our understanding of galaxy evolution. It also includes faint tidal debris created by galaxy mergers, which are key to understanding how gravity, the predominant force in the Universe, shapes galaxy evolution over cosmic time. Put simply, a complete understanding of how the Universe evolves is not possible without a detailed comprehension of the low surface brightness regime.
Astrophysics is currently entering a revolutionary era of new surveys, which not only have large areas but are also incredibly deep. In particular, the Legacy Survey of Space and Time (LSST) and the Subaru Strategic Program from the Hyper Suprime-Cam telescope, are poised to transform our understanding of the Universe, by providing images that are more than 100 times deeper than those from previous surveys. These images will enable us to perform detailed studies of the low surface brightness Universe for the first time.
This project will combine state-of-the-art data from these surveys with in-house cosmological simulations (e.g. NewHorizon) and advanced machine-learning techniques we have developed (e.g. Martin et al. 2020), to perform the first statistical studies of the low-surface-brightness Universe. The project will map the properties of dwarf galaxies in unprecedented detail, over at least half the lifetime of the Universe and quantify the role of key processes like galaxy merging in driving star-formation, black-hole growth and morphological transformation in galaxies over cosmic time.
The student will collaborate closely (through visits and conference trips) with colleagues in Paris, Oxford and a worldwide network of scientists within the international LSST project (in which our team members hold several leadership roles). The project will give the student an excellent skillset in astronomical observation, theory and machine-learning that is well-aligned with this new era of Big Data astronomy.