Modelling stellar activity with Gaussian process regression networks

TL;DR

This paper demonstrates that Gaussian Process Regression Networks can effectively model stellar activity in radial velocity data, improving the detection of Earth-like exoplanets by jointly analyzing RV and activity indicators.

Contribution

It introduces the application of GPRNs to model stellar activity in RV data, showing comparable or improved accuracy over existing methods using three years of solar observations.

Findings

GPRNs can jointly model RV and stellar activity indicators effectively.

Maximum correlation between RV and activity occurs at a few days separation.

Evidence of non-stationary behaviour linked to solar activity cycle.

Abstract

Stellar photospheric activity is known to limit the detection and characterisation of extra-solar planets. In particular, the study of Earth-like planets around Sun-like stars requires data analysis methods that can accurately model the stellar activity phenomena affecting radial velocity (RV) measurements. Gaussian Process Regression Networks (GPRNs) offer a principled approach to the analysis of simultaneous time-series, combining the structural properties of Bayesian neural networks with the non-parametric flexibility of Gaussian Processes. Using HARPS-N solar spectroscopic observations encompassing three years, we demonstrate that this framework is capable of jointly modelling RV data and traditional stellar activity indicators. Although we consider only the simplest GPRN configuration, we are able to describe the behaviour of solar RV data at least as accurately as previously published methods. We confirm the correlation between the RV and stellar activity time series reaches a maximum at separations of a few days, and find evidence of non-stationary behaviour in the time series, associated with an approaching solar activity minimum.