A Gaussian process framework for modelling stellar activity signals in radial velocity data

TLDR

The radial‑velocity method has been highly productive for exoplanet detection, but stellar activity induces variations that can mask or mimic planetary signals, posing a major challenge for next‑generation spectrographs. This study develops a Gaussian‑process framework to model radial‑velocity time series jointly with ancillary activity indicators, aiming to disentangle stellar activity from planetary signals. The framework employs Gaussian processes to jointly fit RV data and activity diagnostics such as bisector spans, line widths, and chromospheric indices, thereby constraining the activity component. The method shows encouraging performance on both synthetic and real datasets, including the publicly available Alpha Centauri B observations.

Abstract

To date, the radial velocity (RV) method has been one of the most productive techniques for detecting and confirming extrasolar planetary candidates. Unfortunately, stellar activity can induce RV variations which can drown out or even mimic planetary signals – and it is notoriously difficult to model and thus mitigate the effects of these activity-induced nuisance signals. This is expected to be a major obstacle to using next-generation spectrographs to detect lower mass planets, planets with longer periods, and planets around more active stars. Enter Gaussian processes (GPs) which, we note, have a number of attractive features that make them very well suited to disentangling stellar activity signals from planetary signals. We present here a GP framework we developed to model RV time series jointly with ancillary activity indicators (e.g. bisector velocity spans, line widths, chromospheric activity indices), allowing the activity component of RV time series to be constrained and disentangled from e.g. planetary components. We discuss the mathematical details of our GP framework, and present results illustrating its encouraging performance on both synthetic and real RV data sets, including the publicly available Alpha Centauri B data set.