Speaker
Description
The dynamics of stars in our galaxy encode crucial information about the Milky Way's dark matter halo. However, extinction from foreground dust can bias studies of stellar populations. By solving the equilibrium collisionless Boltzmann equation with novel machine learning techniques, we estimate the unbiased 6-dimensional phase space density of an equilibrated stellar population and the underlying gravitational potential. Utilizing a normalizing flow-based estimate for the phase space density of stars from the Gaia space observatory, we derive the local gravitational potential of the Milky Way and correct the stellar phase space density for dust extinction. Our data-driven estimates align with recent 3-dimensional dust maps and analytic models of the Milky Way's potential. This measurement will enhance our understanding of the detailed structure and substructure of the Milky Way's dark matter halo.
| Track | Astrophysics |
|---|
