Modern galactic dynamics in the era of plentiful data
Abstract:
Galactic dynamics is no longer a data-starved field of research. With modern and upcoming large-scale surveys and instruments such as SDSS, Gaia and LSST, the quality and quantity of data far exceeds the capacity of modelling methods to digest them. In this course, I review the foundations of galactic dynamics -- the study. of the structure and evolution of galaxies under the force of gravity. Starting from basic concepts such as the Boltzmann equations, I describe various techniques used to infer the gravitational potential of stellar systems from the kinematics of stars: Jeans equations, distribution function-based, particle-based and orbit-based models, galacto-seismology and modelling of stellar streams.
In parallel, I present the panorama of the currently available observational data and the ways to incorporate them into the models. Although mainly concentrating on the Milky Way, I will also discuss the similarities and differences in applications to external, spatially resolved galaxies. Meanwhile, I will offer several simple exercises in analyzing and modelling data from various publicly available real and mock datasets. For these, a laptop with a working Python environment is necessary. The audience is expected to have basic knowledge of classical mechanics, interest in data analysis (not necessarily specific to galactic dynamics), and willingness to develop next-generation modelling approaches.