color maps, stellar ages, stellar multiples

At breakfast I told Morgan Fouesneau (MPIA) my desiderata for a set of matplotlib color maps: I want a map that indicates intensity (dark to bright, say), a map that indicates a sequential value (mass or metallicty or age, say), a map that indicates residuals away from zero that de-emphasizes the near-zero values, and a map that is the same but that emphasizes the near-zero values. I want the diverging maps to never hit pure white or pure black (indeed none of these maps should) because we always want to distinguish values from “no data”. And I want them to be good for people with standard color-blindness. But here's the hard part: I want all four of these colormaps to be drawn from the same general palette, so that a scientific paper that uses them will have a consistent visual feel.

Before lunch, Ness and I met with Marie Martig (MPIA) and Fouesneau to go through our stellar age results. Martig and Fouesneau are writing up a method to use carbon and nitrogen features to infer red-giant ages and masses. Ness and I are writing up our use of The Cannon to get red-giant ages and masses. It turns out that The Cannon (being a brain-dead data-driven model) has also chosen, internally, to use carbon and nitrogren indicators. This is a great endorsement of the Martig and Fouesneau method and project. Because they are using their prior beliefs about stellar spectroscopy better than we are, they ought to get more accurate results, but we haven't compared in detail yet.

Late in the day, Foreman-Mackey and I discussed K2 and Kepler projects. We discussed at length the relationship between stellar multiplicity and binary and trinary (and so on) populations inference. Has all this been done for stars, just like we are doing it for exoplanets? We also discussed candidate (transiting candidate) vetting, and the fact that you can't remove the non-astrophysics (systematics-induced) false positives unless you have a model for all the things that can happen.

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