In this project, the student will use publicly available high-resolution simulations of Milky-Way-like galaxies (e.g. from the APOSTLE, NIHAO, Auriga projects, [1]) and compare the chemo-kinematic properties to new spectroscopic and astrometric observations of millions of stars in the Milky Way. All simulations were tailored to resemble our Galaxy, but which one comes closest when we look at the details?
The mere existence of large stellar datasets and high-resolution simulations is insufficient to ensure a major knowledge gain about the formation and evolution of our Galaxy. Many datasets are subject to non-trivial selection effects, systematic uncertainties (especially for ages of field stars), and correlated errors that impede straightforward conclusions and affect simplistic model-to-data comparisons [2,3]. There is now an urgent need for more quantitative comparisons of Gaia observations to state-of-the-art Milky Way models and clear indications where such models should be improved.
In this project the student will learn to analyse complex observational and simulation data with python, and how to make physical sense of them. Basic knowledge of Galactic astronomy, statistics, and python programming are necessary.