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

TL;DR

This paper introduces a Gaussian process framework for modeling stellar activity signals in radial velocity data, improving the ability to detect exoplanets by disentangling stellar noise from planetary signals.

Contribution

The paper presents a novel Gaussian process approach that jointly models RV data and activity indicators to better separate stellar activity from planetary signals.

Findings

Effective on synthetic datasets

Performs well on real datasets

Enhances planet detection accuracy

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 datasets, including the publicly-available Alpha Centauri B dataset.