A little bit of hooky: I was kidnapped by Aaron Dotter (ANU) and taken up to Mt Stromlo Observatory, to chat with the locals about data and calibration (my favorite subjects these days). Ken Freeman (ANU) came by, and we discussed the just-starting HERMES project, which is on sky and taking data. The project is performing a survey of a million stars at high s/n (like 100) and high-ish resolution (like 30,000 or 50,000). The idea is to do "chemical tagging" and dissect the Milky Way into its accretion-historical parts. There are two challenges we discussed. The first is calibration and extraction of the spectra, which must deal with cross-talk between fibers (yes, it is fiber-fed like APOGEE) or their traces on the CCD, modeling and removal of sky in wavelength regions where there are no sky lines, and determination of the point-spread and line-spread functions as a function of position in the device. The second is the chemical tagging problem in the limit that models are good but not perfect. I have many ideas about that; they fall into the category we talked about with Sontag (NYU) last week: Simultaneously we want to use the models to understand the data and the data to update the models.
In the meeting today I missed some of the talks, of course, which I regret. There were talks in the morning about the possibility that not only does nature (in equilibrium) maximize entropy, but perhaps when it is out of equilibrium it also maximizes entropy production. I actually worked on this as an undergraduate back around 1991 with Michel Baranger (MIT emeritus); back then we were suspicious that the principle could even work. I think now it is known that for some systems, in some circumstances, they do seem to choose the path of maximum dissipation, but the general value of the principle is not clear; it might even be more misleading than useful.
Iain Murray (Edinburgh) gave a great talk (clear, surprising, useful), about inferring density functions (probability distributions) given points. He showed some amazing results for "autogregressive" models, which are so much more general than what I thought they were this summer when we were working on Kepler. He gave me a lot of new ideas for MJ Vakili's project.