GPs for p-modes in RV data

I checked in with Megan Bedell (Flatiron) on our projects today. She showed really nice results in which she fits simulated radial-velocity data for a star that is oscillating in finite-coherence asteroseismic modes. My loyal reader knows that we have been working on this! But the cool thing is that she can mow fit the oscillations with a Gaussian Process with a kernel that is roughly correct, or exactly correct, even when the observations are integrated over finite exposure times. That's a breakthrough. It depends in large part on the magic of exoplanet.

Now GPs are extremely flexible, so the question is: How to validate results? After all, any GP can thread through any set of points. We came up with two schemes. The first is a N-fold cross-validation, in which we train the GP on all but 1/N-th of the data and then predict that 1/N-th, and cycle to get everything. First experiments along these lines seem to show that the more correct the kernel, the better we predict! The second is that we make fake data that includes the p-modes and a simulated planet companion. We show that our planet-companion inferences become more accurate as our kernel becomes more accurate.

We're hoping to improve on the results of this paper on p-mode mitigation. My conjecture is that when we use an accurate GP kernel, we will get exoplanet inferences at any exposure time that are better than one gets using the quasi-optimal exposure times and a “jitter” term to account for the residual p-mode noise.