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New photometric space missions to detect and characterise transitingexoplanets are focusing on bright stars to obtain high cadence, highsignal-to-noise light curves. Since these missions will be sensitive to stellaroscillations and granulation even for dwarf stars, they will be limited bystellar variability. We tested the performance of Gaussian process (GP)regression on the characterisation of transiting planets, and in particular todetermine how many components of variability are necessary to describe highcadence, high signal-to-noise light curves expected from CHEOPS and PLATO. Wefound that the best GP stellar variability model contains four to fivevariability components: one stellar oscillation component, two to fourgranulation components, and/or one rotational modulation component. This highnumber of components is in contrast with the one-component GP model (1GP)commonly used in the literature for transit characterisation. Therefore, wecompared the performance of the best multi-component GP model with the 1GPmodel in the derivation of transit parameters of simulated transits. We foundthat for Jupiter- and Neptune-size planets the best multi-component GP model isslightly better than the 1GP model, and much better than the non-GP model thatgives biased results. For Earth-size planets, the 1GP model fails to retrievethe transit because it is a poor description of stellar activity. The non-GPmodel gives some biased results and the best multi-component GP is capable ofretrieving the correct transit model parameters. We conclude that whencharacterising transiting exoplanets with high signal-to-noise ratios and highcadence light curves, we need models that couple the description of stellarvariability with the transits analysis, like GPs. Moreover, for Earth-likeexoplanets a better description of stellar variability improves the planetarycharacterisation.

Improving transit characterisation with Gaussian process modelling of stellar variability