Galactic astronomy is experiencing a revolutionary increase in available data. A large number of international surveys, like the Gaia satellite mission, is mapping the positions, motions, and properties of our galaxy’s stars as well as external galaxies from ground and space, to reveal the structure, dynamics and history of our own Galaxy and compare it to disc galaxies in general. The number of stars for which we have good information on position, motion, and surface composition (which tells us where a star came from), has increased by a factor 10^4 or 10^5 compared to what we had 10 years ago. These data can only be fully understood with statistical methods and detailed chemodynamical models. MSSL/UCL has unique competence in both understanding the data from modern surveys and to apply them to constrain e.g. the distribution of dark matter, to understand the detailed structure, e.g. of the Galactic bar and spiral arms, and the history of the stellar populations within the Galaxy's disc and halo.
We want to understand the current structure and evolution of the Milky Way. With such a model, we can also map the dark matter content of our galaxy or probe other models of gravity. In the later stages, we want to compare this knowledge to other galaxies, using surveys of extra-galactic field spectroscopy.
But how can we understand these data? Simply looking at observed motions of stars will not solve the problem, since we need the full distribution of stars in ages, surface abundances, and kinematics, to resolve the observational biases, in particular selection effects, since stars in a survey are not a representative sample of the Milky Way’s stellar population and these biases change with position.
One could now attempt to rely on “full” simulations. However, large simulations (i.e. N-body or N-body + hydrodynamic models) are too costly to run a sufficient number of them for fitting their internal parameters or fitting to the observational data. On the other hand, very simple analytical models or directly analysing data are severely limited by the presence of strong selection biases: the stars ending up in a survey are not a representative sample of the populations in a galaxy, and this selection changes with position.
Our group attempts the middle path, and has already developed comprehensive analytical models that cover both the chemical and structural evolution of a Galaxy. The PhD student can rely on these models, expand them and apply them to the data. In the long run, the model can also be expanded to directly incorporate direct spectroscopic fitting to make sense extragalactic spectroscopic observations, which will be particularly attractive with the advent of the next generation of large telescopes: So far, the standard way is to extract simple statistical moments (mean, dispersion) for e.g. the stellar velocity distribution in these objects directly from spectra. Directly analysing the data will remove this problem and allow us to directly measure the dynamics and histories of these disc galaxies and to compare them to what we learn from the Milky Way.
Desired Knowledge and Skills
• Undergraduate in a subject of physics
• Strong computational skills and/or willingness to learn
• Good analytical skills
Applications submitted by 31st January 2020 will be given full consideration. We will continue accepting applications until all places are filled. After we receive your application, we will select candidates for interviews. If you are selected, you will be invited for an interview at MSSL. You will have the opportunity to see the laboratory, students' flats and talk to current students. The studentships are for the advertised projects only. In your application, please specify which project you want to apply for.
To apply, please visit the Online Application page, select department of "Space & Climate Physics" and programme type of "Postgraduate Research". After pushing "Search Now" button, select "RRDSPSSING01: Research Degree: Space and Climate Physics" for Full-time or Part-time mode.
Our Online Applications page can be found here: https://www.ucl.ac.uk/adminsys/search/
An upper second-class Bachelor’s degree, or a second-class Bachelor’s degree together with a Master's degree from a UK university in a relevant subject, or an equivalent overseas qualification.
Students from the UK or those from the EU who meet the residency requirements (3 years' full-time residency in the UK) are potentially eligible for a Science and Technology Facilities Council (STFC) studentship.