Today was a whirlwind of meetings and sessions. I started with a great conversation with Vasiliev (Cambridge) and Valluri (Michigan) about statistical inference problems related to Schwarzschild modeling a galaxy using an orbit library. I'm not sure I helped much! Right now no-one knows how to realistically marginalize out the effective distribution function, although I think there might be good ideas somewhere in the probabilistic machine-learning world.
We had a plenary discussion of non-steady-state and non-equilibrium aspects of the Milky Way and how we will model or understand them. Fundamentally, we only know how to infer the dynamics of the Milky Way by making strong assumptions: Either (in the case of Jeans or Schwarzschild modeling, say) that there is time symmetry and also cylindrical or spherical symmetries, or (in the case of stream modeling, say) that the stars were put onto orbits in some collectively informative way. Since the real Milky Way violates these assumptions for most stars at some level, we need qualitatively new kinds of assumptions to make. My proposal: That the Milky Way grew from the cosmological initial conditions! That's the right thing, I think, but we don't yet have any tractable way to think about (say) the Gaia data in that context. At least not precisely.
In the afternoon, there was a cross-meeting dark-matter session in which a large set of particle physicists and a large set of astrophysicists interacted over testing dark-matter models. I learned that there is a huge literature we don't know enough about. I am very interested in going down this path, because it connects Gaia and SDSS-V to fundamental properties of the Universe. That's what I would really love to do (and that's in part why my original idea for SDSS-V was called "Disco": Cosmology with the disk).
At some point in the day, I realized that we can test chemical-tangents method (my baby) in fake data! I discussed this with Loebman (Davis) and Price-Whelan (Princeton). I also realized that we can compare it to Jeans modeling, and show (I hope) that it always wins.
At the very end of the day, I had a conversation with Anderson (Flatiron) about advising and mentoring of postdocs. I feel very lost, I have to say: I want to give my attention to all my projects, my attention and time are limited, I don't spend my attention in the right places, I disappoint many of my people, and I impede their progress. I feel like I am doing it wrong! I don't feel like I understand how to be a mentor, and I am starting to feel stressed about it. One of the strange things is that the postdocs with whom I work are both the best and most fun collaborators I have, and also independent, capable scientists. That would seem to make it all easy and fun, but instead it somehow makes it confusing and existential. I went home unhappy from an absolutely great week in Aspen.