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The study of exoplanetary atmospheres epitomises a continuous quest forhigher accuracy measurements. Systematic effects and noise associated with boththe stellar activity and the instrument can bias the results and thus limit theprecision of the analysis. To reach a high photometric and spectroscopicprecision, it is therefore essential to correct for these effects. We presenthere a novel non-parametric approach, named Gaussian Process method for StarCharacterization (GPSC), to remove effects of stellar activity and instrumentalsystematics on planetary signals, with a view to preserve the atmosphericcontribution which can be as small as 10$^{-4}$ or even 10$^{-5}$ the flux ofthe star. We applied our method to data recorded with Kepler, focussing on asample of lightcurves with different effective temperatures and fluxmodulations. We found that GPSC can very effectively correct for the short andlong term stellar activity and instrumental systematics. Additionally we runthe GPSC on both real and simulated transit data, finding transit depthsconsistent with the original ones. Consequently we considered 10 hours ofcontinuous observations: daily, every other day and weekly, and we used theGPSC to reconstruct the lightcurves. When data are recorded more frequentlythan once every five days we found that our approach is able to extrapolate thestellar flux at the 10$^{-4}$ level compared to the full stellar flux. Theseresults show a great potential of GPSC to isolate the relevant astrophysicalsignal and achieve the precision needed for the correction of short and longterm stellar activity.

Gaussian Process for star and planet characterisation