Milky Way Dust and Dynamics

Speaker: Greg Green (MPIA) In Person and zoom 

Zoom info: https://stanford.zoom.us/my/sihanyuan?pwd=QnpsUHZWWGJ2ekVYWmZVL3BmM0gzZ…

The gravitational potential of the Milky Way is generated by all of the matter - both baryonic and dark. By mapping the potential, we can thus uncover the distribution of the unseen dark component of the Milky Way. Gaia has precisely measured 6D phase-space coordinates of over 30 million stars, dramatically expanding our knowledge of stellar kinematics in the Milky Way. Previous methods of recovering the gravitational potential from stellar kinematics have made use of highly simplified models, but the quality of the new phase-space data provided by Gaia demands new approaches that can more fully describe the richness of the data. I will discuss a new method, "Deep Potential," which applies computational tools from Deep Learning in a physically principled way to solve the collisionless Boltzmann equation and recover the underlying gravitational potential.Any work on the Milky Way inevitably runs into the problem of dust extinction, and the recovery of the gravitational potential is no exception. Despite the vital importance of interstellar dust to many areas of astronomy, its composition remains highly uncertain. However, low-resolution spectroscopy from Gaia is enabling a transformation of our understanding of dust properties. The dust extinction curve, typically characterized by R(V), depends on both the composition and size distribution of dust grains. I will discuss the first all-sky 3D map of dust R(V), based on 130 million stellar measurements. This map not only allows more accurate extinction corrections, but also sheds light on the chemical evolution of the interstellar medium.Both of these areas of Milky Way research borrow tools from Deep Learning - applied in physically motivated ways - and make extensive use of Gaia data. I will discuss some lessons on the use of such tools, in and beyond Milky Way research.