Galaxy growth and evolution over the last ~8 billion years

Disk galaxies with masses similar to that of the Milky Way are very common in the Universe, and they build up to their masses both by forming new stars in situ and by absorbing the stars from smaller satellite galaxies. Such "minor" (and even "micro") mergers are likely responsible for a substantial amount of galaxy growth across the full population. The tidal streams from these interactions also remain coherent for billions of years and are extremely valuable as archaeological remnants of galactic formation processes. However, very little is understood about the effect of these minor interactions on galaxy evolution as a whole, because the unique properties of their tidal signatures present significant challenges to both observing them in real galaxies and modelling them with computer simulations.

With the latest generation of survey telescopes and high-resolution cosmological models, however, that is about to change. This project will therefore combine cutting-edge observational, theoretical and statistical approaches to understanding the growth of disk galaxies via mergers. A key aim is to develop a new analytical tool for quantitatively constraining the merger history of galaxies given their observed tidal features. This will involve hands-on work with large datasets as well as working with and writing code. This analysis package will be of significant interest to the astrophysical community, as will the project's results applying that code to the latest data from current surveys such as the Dark Energy Survey and the Large Synoptic Survey Telescope commissioning surveys, which will commence during the term of this project.

The student will join multiple established, productive communities, such as the Galaxy Zoo and Horizon-AGN projects. They will likely also have the opportunity to gain hands-on observing experience at world-class telescopes.

This project would suit someone with some experience of writing code (Python, Java etc) and, more importantly, with a strong interest in developing that skill for use in statistical analysis of large data sets.