Dynamical modelling of dwarf spheroidal galaxies

The dwarf spheroidal (dSph) satellites of the Milky Way are the most dark matter dominated stellar systems known and, consequently, are widely recognised as valuable laboratories in which to study the properties of dark matter. Observations of the stellar kinematics have yielded data sets of several thousand individual stellar velocities in several dSphs. However, dynamical modelling of these data has to date been based on very restrictive simplifying assumptions (sphericity, equilibrium).

This project will develop a new approach to the modelling of dSphs. We will use Gaussian Process Emulation to enable us to explore more general model parameter spaces than have been possible using more traditional modelling schemes (such as Markov-Chain-Monte-Carlo). The underlying models may be static, distribution function based models or N-body simulations.