Fadely's calibration projects are evolving from k-nearest-neighbor to kernel density estimates. We spent some time today trying to figure out how KDEs are implemented in practice in dimensions larger than 1. Almost all the discussion on the web uses one-dimensional examples, which are cool and instructive, but there are a few details about generalization to higher dimension that are non-trivial. I think we figured them all out by the end of the day.
Foreman-Mackey found some craziness with the eccentricities in his Kepler fits. These were puzzling for a while: Kepler data is only minimally sensitive to eccentricity. But then Foreman-Mackey remembered the obvious point that if you fix the parent-star mass and radius, then you can constrain the eccentricity. That's even been done for real systems.