George E. Valley Jr. Prize: Shining Light on Dark Matter

In this talk, I discuss how stellar kinematics can be used as tracers for Dark Matter in the Milky Way, particularly in the solar neighborhood, the Galactic center, and dwarf galaxies. I will emphasize the role of machine learning techniques in uncovering the physics behind the tremendous new data in high resolution simulations and observations from Gaia. I will discuss the local kinematic stellar structures found in the solar neighborhood, the updated measurement of the circular velocity of the Milky Way using data driven models, and a novel Graph Neural Network-based method for the extraction of the density profile of Dark Matter in dwarf galaxies. I will close by discussing how these measurements can be a window in the particle nature of Dark Matter.