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.
The progress of modern astronomy is comparable to breakthroughs like the invention of the microscope in biology. Until 10 years ago, the largest spectroscopic samples just comprised a few hundred stars, with spectra being painfully collected and analysed by hand. The modern surveys have given us sample sizes of >10^8 stars with photometry and kinematics, and ~10^7 stars with detailed spectroscopic information. These allow us to determine stellar surface abundances (encoding where a star comes from), surface temperatures and surface gravities (providing stellar ages). However, we cannot just read out these data and draw a picture of our galaxy: uncertainties and biases in these datasets are of a similar size as the signals/differences between populations, and so in a naive analysis most apparent structures will not be real, but be caused by errors in data and models. So, to make sense of these surveys, we need a full analysis of all available information with full assessment of uncertainties and biases.
Our group has developed the first fully integrated Bayesian framework to estimate stellar parameters from stellar spectra, has advanced statistical techniques to control the resulting parameter estimates, in particular for stellar distances, and has experience with machine learning techniques. The PhD student expanding these tools will be able to map stellar populations throughout the Milky Way, and can challenge existing models of stellar atmospheres and stellar evolution. The student can also rely on the group’s expertise to explore the formation and structure of our Milky Way and other galaxies.
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.