Speaker
Sung Hak Lim
(Rutgers University)
Description
Mapping the Milky Way’s dark matter requires moving beyond traditional, rigid dynamical models. In this talk, generative models—specifically Normalizing Flows— are used to learn the stellar phase space distribution directly from Gaia data. This approach enables a flexible, model-independent reconstruction of the Galactic gravitational potential and local dark matter density. These data-driven techniques provide a promising avenue to handle complex observational biases and what they reveal about the dark sector's influence on our Galaxy.