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ABSTRACT Long, high-quality time-series data provided by previous space missions such as CoRoT and Kepler have made it possible to derive the evolutionary state of red giant stars, i.e. whether the stars are hydrogen-shell burning around an inert helium core or helium-core burning, from their individual oscillation modes. We utilize data from the Kepler mission to develop a tool to classify the evolutionary state for the large number of stars being observed in the current era of K2, TESS, and for the future PLATO mission. These missions provide new challenges for evolutionary state classification given the large number of stars being observed and the shorter observing duration of the data. We propose a new method, Clumpiness, based upon a supervised classification scheme that uses ‘summary statistics’ of the time series, combined with distance information from the Gaia mission to predict the evolutionary state. Applying this to red giants in the APOKASC catalogue, we obtain a classification accuracy of $\sim 91{{\ \rm per\ cent}}$ for the full 4 yr of Kepler data, for those stars that are either only hydrogen-shell burning or also helium-core burning. We also applied the method to shorter Kepler data sets, mimicking CoRoT, K2, and TESS achieving an accuracy $\gt 91{{\ \rm per\ cent}}$ even for the 27 d time series. This work paves the way towards fast, reliable classification of vast amounts of relatively short-time-span data with a few, well-engineered features. 

The analysis of photometric time series in the context of transiting planet surveys suffers from the presence of stellar signals, often dubbed ‘stellar noise’. These signals, caused by stellar oscillations and granulation, can usually be disregarded for main-sequence stars, as the stellar contributions average out when phase-folding the light curve. For evolved stars, however, the amplitudes of such signals are larger and the timescales similar to the transit duration of short-period planets, requiring that they be modelled alongside the transit. With the promise of TESS delivering of the order of ∼105 light curves for stars along the red giant branch, there is a need for a method capable of describing the ‘stellar noise’ while simultaneously modelling an exoplanet’s transit. In this work, a Gaussian process regression framework is used to model stellar light curves and the method validated by applying it to TESS-like artificial data. Furthermore, the method is used to characterize the stellar oscillations and granulation of a sample of well-studied Kepler low-luminosity red giant branch stars. The parameters determined are compared to equivalent ones obtained by modelling the power spectrum of the light curve. Results show that the method presented is capable of describing the stellar signals in the time domain and can also return an accurate and precise measurement of νmax, i.e. the frequency of maximum oscillation amplitude. Preliminary results show that using the method in transit modelling improves the precision and accuracy of the ratio between the planetary and stellar radius, Rp/R⋆. The method’s implementation is publicly available.1