A comprehensive analytical model of tidal stripping

Throughout the evolution of our Universe, galaxies grow in a hierarchical manner and accrete many smaller structures through gravity. Most of these smaller structures end up orbiting around their host galaxy as "satellite galaxies" like the Small and Large Magellanic Clouds. Such satellites get exposed to strong gravitational tidal fields which make them lose large fractions of their mass a process that is called "tidal stripping". Accurately predicting such tidal stripping processes is essential to understand the evolution of galaxies and to predict the distribution of the invisible dark matter. So far, it is only possible to make reliable predictions of tidal stripping processes through so called "N-body simulations" which follow a large number of particles and their evolution in gravitational fields. While these simulations are accurate, they are also quite expensive and difficult to comprehend. It is the goal of this project to develop a new method to solve the tidal stripping problem analytically from first principles. The core idea of this new approach is that tidal stripping removes mass sufficiently slowly that the affected satellite reacts adiabatically meaning in an entropy- conserving manner. In this adiabatic approximation it becomes possible to calculate the result of the stripping process analytically without simulations. The method will enable new physical insights into the tidal stripping problem and it will offer a cheap and accurate alternative to N-body simulations. Furthermore, it will make it possible to predict the distribution of structures that are far beyond the resolution limit of simulations. According to the current standard model of cosmology (the "Lambda-CDM model") it is expected that countless clumps of the invisible dark matter form throughout our Universe. The new model will be able to accurately predict their evolution through gravitational tides and their distribution at later times. Thereby it will help to understand where and how to optimally search for dark matter and it will contribute to inferring the properties of our Universe from ongoing large scale structure surveys, such as EUCLID.