Lina Necib, MIT
Title: (Machine) Learning of Dark Matter
In this talk, I explore the impact of stellar kinematics on understanding the particle nature of Dark Matter, overviewing the correlations between stellar and Dark Matter phase space distributions in four separate locations: the solar neighborhood, the Galactic center, dwarf galaxies, and streams. I will then focus on the use of machine learning techniques applied to data from the Gaia mission to disentangle the local kinematics substructures, and the use of simulations to study the correlations between stars and Dark Matter. I will end by relating these empirical measurements to Dark Matter detection experiments.