Today was day one of #ExoStat, hosted by Jessi Cisewski at CMU. The meeting is a follow-up to the #ExoSamsi meeting we held last summer at SAMSI, bringing together statisticians and exoplaneteers. The meeting was structured with talks in the morning and hacking in the afternoon. Two highlights in the morning talks were the following:
Dawson (UCB) talked about building flexible noise models for the Kepler lightcurves, and showing how those flexible noise models improved discovery and measurements of exoplanets. One of the most intriguing results in her talk was that she finds that impact parameter measurements are very biased or unstable, in the sense that she gets different answers with different priors; Foreman-Mackey and I have found the same recently. For Dawson this is critical, because she has identified that she can potentially say things about planetary system inclination distributions by measuring impact parameter variations.
Ian Czekala (CfA) spoke about flexible likelihood functions (noise models) for fitting stellar spectra. He finds, as many do, that though spectral models are incredibly good, there are smooth calibration issues and also individual lines that are slightly wrong. He has a covariance matrix (a non-stationary Gaussian process covariance matrix) that handles both of these things. On the "bad lines" issue, he puts in (for each bad line) a rank-one contribution to the covariance matrix that adds variance with the proper shape to be a varying line. This is a simple and beautiful idea. After his talk, we discussed modeling covariant groups of lines, and even how to discover such groups automatically. The long-term goal is automatic data-driven improvement of the spectral models.