Modelling stellar activity with Gaussian process regression networks

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.

30 pages, 23 figures, accepted for publication on MNRAS

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Cited by in corpus (5)

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• $\text{Wapiti}$: a data-driven approach to correct for systematics in RV data -- Application to SPIRou data of the planet-hosting M dwarf GJ 251
• A statistical model of stellar variability. I. FENRIR: a physics-based model of stellar activity, and its fast Gaussian process approximation
• TILARA: Template-Independent Line-by-line Algorithm for Radial velocity Analysis. I. Description of the code and application on a Sun-like star
• The SPIRou Legacy Survey: near-infrared and optical radial velocity analysis of Gl 480 and Gl 382 using SPIRou, HARPS and CARMENES spectrographs