carbon stars, data-driven models, simulating faint galaxies

In the morning I hosted our weekly stars meeting at the CCA. Jill Knapp (Princeton) came in! She talked to us about carbon stars, why they are interesting, where they are found, and what we might learn about them from Gaia DR1. We proposed that we could get her results pretty fast! Kathryn Johnston (Columbia) talked to us about the use of stellar models to get very precise distances to all kinds of stars, which triggered many conversations. One is whether and how we could make sure we have such technology up and running at the Gaia Sprint in 1.5 weeks. Another is whether we could get percent-level distances to stars without using stellar models. That is part of my evil plans.

In the afternoon I hosted our weekly cosmology meeting. Lauren Anderson (CCA) talked about her matched dark-matter-only and baryonic (SPH) simulations, and how she is unsatisfied with how they understand the faint end of the galaxy population. How can we understand the completeness of the simulations, or get the most out of the galaxies that are at the low-mass end? We discussed this and the conversation edged into halo-occupation territory. I then started saying crazy stuff about machine learning and everyone quietly left the room, claiming other commitments and, oh my, look at the time!

1 comment:

  1. Long time no talking to you, David.

    I share your pain. Everybody wants thousands of this, millions of that, but no one wants to know the technical side.