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Gaussian processes for modelling stellar activity and detecting planets

Speaker: Vinesh Rajpaul (Oxford)

Abstract

To date, 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 effects. 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 will present briefly a GP framework developed to model 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 talk briefly about how it is used in practice, and demonstrate its performance using both synthetic and real data sets.

The role of model selection, summary statistics and multiple-point statistics in Bayesian inverse problems: Examples from geophysics.
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