refactoring, seminar technique, search by modeling

In parallel working session this morning (where collaborators gather in my office to work together), Montet, Bedell, and I worked out a re-factor of the RV code they have been working on, in order to make it more efficient and easier to maintain. That looked briefly like a big headache and challenge, but in the end the re-factor got completely done today. Somehow it is brutal to consider a refactor, but in the end it is almost always a good idea (and much easier than expected). I'm one to talk: I don't write much code directly myself these days.

Sarah Pearson (Columbia) gave the NYU Astro Seminar today. It was an excellent talk on what we learn about the Milky Way from stellar streams. She did exactly the right thing of spending more than half of the talk on necessary context, before describing her own results. She got the level of this context just right for the audience, so by the time she was talking about what she has done (which involves chaos on the one hand, and perturbations from the bar on the other), it was comprehensible and relevant for everyone. I wish I could boil down “good talk structure” to some simple points, but I feel like it is very context-dependent. Of course one thing that's great about the NYU Astro Seminar is that we are an interactive audience, so the speaker knows where the audience is.

After lunch I had a great and too-short discussion with Robyn Sanderson (Caltech), continuing ideas that came up on Wednesday about search for halo substructure. We discussed the point that when you transform the data to something like action space (or indeed do any non-linear transformation of the data), the measurement uncertainties become crazy and almost impossible to marginalize or even visualize. Let alone account for properly in a scientific analysis. So then we discussed whether we could search for substructure by transforming orbits into the data space and associating data with orbits, in the space where the data uncertainties are simple. As Sanderson pointed out, that's Schwarzschild modeling. Might be a great idea for substructure search.