Kepler, uncertainties, inference

Bernhard Schölkopf showed up for the day today. He spent the morning working with Foreman-Mackey on search and the afternoon working with Wang on self-calibration of Kepler. In the latter conversation, we hypothesized that we might improve Wang's results if we augment the list of pixels he uses as predictors (features) with a set of smooth Fourier modes. This permits the model to capture long-term variability without amplifying feature noise.

Before that, in group meeting, Sanderson told us about the problem of assigning errors or uncertainties to our best-fit potential in her method for Milky Way gravitational potential determination. She disagrees with the referee and I agree with the referee. Ultimately, we concluded that the referee (and I) are talking about precision, but the accuracy of the method is lower than the precision. I think we understand why; it has something to do with the amount of structure (or number of structures) in phase space.

At lunch, we met up with David Blei (Columbia) and shot the shih about data science and statistics. Blei is a probabilistic inference master; we convinced him that he should apply his super-powers towards astrophysics. He offered one of our party a postdoc, on the spot!

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