Fadely and Foreman-Mackey are both having fitting issues that are hard to comprehend, both in extremely ambitious comprehensive data analysis programs. Fadely has a model where the update steps (the hand-built optimization steps) are guaranteed (by math) to improve the objective function and yet, and yet! I asked him for a document, so we can compare code to document. His model is a beautiful one, which simultaneously finds the position and flux of every star in the HST data for the WFC3 IR channel, the point-spread function, and the pixel-level flat-field!
Foreman-Mackey is finding that his automatic re-fits (samplings using emcee and interim priors) to all Kepler Objects of Interest are favoring high impact parameters. This is a generic problem with exoplanet transit fits; the KOI best-fit values have these biases too; that doesn't make it trivial to understand. Even our hierarchical Bayesian inference of the impact-parameter distribution is not okay. It has something to do with the prior volume or else with the freedom to fit at larger impact parameter; or perhaps a (wrong) lack of penalty for large planets. Not sure yet. We have some hypotheses we are going to test (by looking at the samplings and prior-dependences) tomorrow.
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