Today Lauren Anderson (Flatiron) and Adrian Price-Whelan (Princeton) made beautiful visualizations of Anderson's 20-million star catalog with distances, built by training a model on the TGAS Catalog and applying it to plausibly-red-clump stars in the billion-star catalog from Gaia. I give an example below, which shows two thin slices of the Milky Way, one through the Sun, and one through the Galactic Center (but blotted out by local dust).
Andy Casey (Monash) got our asteroseismology project working with real data! He sub-sampled some Kepler light curves down to something like Gaia end-of-mission cadence, and then applied the Stephen Feeney (Flatiron) likelihood function. Again, it has peaks at reasonable asteroseismic parameters, near the KASC published values. We are slowly developing some intuitions about what parameters are well constrained and where.
After four days of hacking on The Cannon but with probabilistic (noisy and missing) labels, Christina Eilers (MPIA) and I gave up: We worked out the bugs, got the optimizer working, and realized that our issues are fundamentally conceptual: When you have a bad model for your data (that is, a model that is ruled out strongly by the data), there can be conflicts between model accuracy and prediction accuracy. We have hit one of those conflicts. We need to re-group on this one.