Dynamical modelling of dwarf spheroidal galaxies

银河系的矮球体（dSph）卫星是已知的暗物质占主导地位的恒星系统，因此被广泛认为是研究暗物质特性的有价值的实验室。通过对恒星运动学的观测，我们得到了在几个dsph内数千个独立恒星速度的数据集。然而，迄今为止，这些数据的动态建模是基于非常严格的简化假设（球形、平衡）。这个项目将开发一种新的方法来模拟哲学哲学。我们将使用高斯过程仿真，使我们能够探索更一般的模型参数空间，而不是使用更传统的建模方案（如马尔可夫链蒙特卡罗）。底层模型可能是静态的、基于分布函数的模型或n体模拟。

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.