Abstract

The radial-velocity (RV) method has been one of the most productive techniques for detecting extrasolar planetary candidates. Unfortunately, stellar activity can induce RV variations which can drown out or even mimic planetary signals, and it is extremely difficult to model and thus mitigate these stellar signals. This is expected to be a major obstacle to using next-generation instruments to detect lower mass planets, planets with longer periods, and planets around more active stars. Enter Gaussian processes (GPs), which have a number of attractive features that make them very well suited to the joint modelling of stochastic activity processes and dynamical (e.g. planetary) signals. In this talk I’ll present a GP-based framework for modelling RV time series jointly with ancillary activity indicators, allowing the activity component of RV time series to be constrained and disentangled from planetary components. I will discuss its use in practice, and demonstrate its performance using both synthetic and real data sets. I will also sketch a few other recent and promising applications of GPs in the context of exoplanet detection, including precise extraction of RV shifts directly from observed spectra; characterising differential stellar rotation; and modelling in a systematic way the effects of an observing window function, as proved relevant e.g. to the case of Alpha Centauri B.