At MPIA Galaxy Coffee, Foreman-Mackey spoke about his very robust calibration model for SDSS Stripe 82. The model is a mixture of variable and non-variable stars, and for each, the observations are treated as a mixture of good data and bad data. These mixture models are effectively marginalizations over classifications; they don't produce or require hard classifications of stars into variable and non-variable, nor measurements into good and bad. This way, they take the information they can get from everything, but learn the most from the best data. The calibration model looks great. On his RR Lyrae finding, it occurred to us that maybe we need to model the stochastic as well as the periodic variations if we are going to be as sensitive as possible: On long time scales, RR Lyrae lightcurves evolve.
Late in the day, at the schwimmbad, I got my orbit-fitting code to use proper Python classes, and have the potential models inherit functions from a generic potential class. That made the code much nicer and easier to extend and test.
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