First thing in the morning I spoke with Scott Singer (NYU) about confirming or checking the ultra-short-period exoplanets shown to use by Sanchis-Ojeida. He is new to it all, so we talked about where the data are, how they are indexed and named, and how to plot them.
At the very end of the day, I pitched the project of making probabilistic models that can generate stellar variability (consistent with observations) to our visitor Andrew Gordon Wilson (CMU). The idea is to use all the light curves we have ever seen to build a family of non-trivial kernels (what's called “kernel learning” in the machine-learning literature) and a prior over those, so that we can model (in my sense, which involves a likelihood function) any stellar variability with a bespoke Gaussian Process. This is the key missing piece in our plans to take Kepler (or Kepler-like) light curves and separate them into the component generated by stellar variability, the component generated by spacecraft variability, and the component generated by any transiting companion: We need a good model of what stars can do!