calibration and search in Kepler; replacing humans

In group meeting we went through the figures for Dun Wang's paper on pixel-level self-calibration of Kepler, figure by figure. We gave him lots of to-do items to tweak the figures. This is all in preparation not just for his paper but also for AAS 225, which is in early January. At the end we asked "Does this set of figures tell the whole story?" and Fadely said "no, they don't show general improvement across a wide range of stars". So we set Wang on finding a set of stars to serve as a statistical "testbed" for the method.

Also in group meeting, Foreman-Mackey showed some of the systems returned by his search for long-period planets in the light-curves of bright Kepler G dwarf stars. So far it looks like he doesn't have anything that Kepler doesn't have, but he also thinks that some of the Kepler objects might be false positives.

We spent some time looking at Huppenkothen's attempt to reproduce human classification of states of black hole GRS 1915 using machine learning. We scoped a project in which she finds the features that do the best job of reproducing the human classification and then does an unsupervised clustering using those features. That should do something similar to the humans but possibly much better. She has good evidence that the unsupervised clustering will lead to changes in classes and classifications.

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