We present the discovery of HD 221416 b, the first transiting planet identified by the Transiting Exoplanet Survey Satellite (TESS) for which asteroseismology of the host star is possible. HD 221416 b (HIP 116158, TOI-197) is a bright (V=8.2 mag), spectroscopically classified subgiant that oscillates with an average frequency of about 430 μHz and displays a clear signature of mixed modes. The oscillation amplitude confirms that the redder TESS bandpass compared to Kepler has a small effect on the oscillations, supporting the expected yield of thousands of solar-like oscillators with TESS 2 minute cadence observations. Asteroseismic modeling yields a robust determination of the host star radius (R å =2.943±0.064 R e), mass (M å =1.212±0.074 M e), and age (4.9±1.1 Gyr), and demonstrates that it has just started ascending the red-giant branch. Combining asteroseismology with transit modeling and radial-velocity observations, we show that the planet is a "hot Saturn" (R p =9.17±0.33 R ⊕) with an orbital period of ∼14.3 days, irradiance of F=343±24 F ⊕ , and moderate mass (M p =60.5±5.7 M ⊕) and density (ρ p =0.431±0.062 g cm −3). The properties of HD 221416 b show that the host-star metallicity-planet mass correlation found in sub-Saturns (4-8 R ⊕) does not extend to larger radii, indicating that planets in the transition between sub-Saturns and Jupiters follow a relatively narrow range of densities. With a density measured to ∼15%, HD 221416 b is one of the best characterized Saturn-size planets to date, augmenting the small number of known transiting planets around evolved stars and demonstrating the power of TESS to characterize exoplanets and their host stars using asteroseismology.

Context. New photometric space missions to detect and characterise transiting exoplanets are focusing on bright stars to obtain high cadence, high signal-to-noise light curves. Since these missions will be sensitive to stellar oscillations and granulation even for dwarf stars, they will be limited by stellar variability. Therefore, it is crucial and timely to develop robust methods to account for and correct for stellar variability. Aims. We tested the performance of Gaussian process (GP) regression on the characterisation of transiting planets, and in particular to determine how many components of variability are necessary to describe high cadence, high signal-to-noise light curves expected from CHEOPS and PLATO. To achieve this, we selected a sample of bright stars observed in the asteroseismology field of CoRoT at high cadence (32 sec) and high signal-to-noise ratio. Methods. We used GPs to model stellar variability including different combinations of stellar oscillations, granulation, and rotational modulation models. We preformed model comparison to find the best activity model fit to our data. We compared the best multicomponent model with the usual one-component model used for transit retrieval and with a non-GP model. Results. We found that the best GP stellar variability model contains four to five variability components: one stellar oscillation component, two to four granulation components, and/or one rotational modulation component, which is consistent with results from asteroseismology. However, this high number of components is in contrast with the one-component GP model (1GP) commonly used in the literature for transit characterisation. Therefore, we compared the performance of the best multi-component GP model with the 1GP model in the derivation of transit parameters of simulated transits. We found that for Jupiter-and Neptune-size planets the best multi-component GP model is slightly better than the 1GP model, and much better than the non-GP model that gives biased results. For Earth-size planets, the 1GP model fails to retrieve the transit because it is a poor description of stellar activity. The non-GP model gives some biased results and the best multi-component GP is capable of retrieving the correct transit model parameters.Conclusions. We conclude that when characterising transiting exoplanets with high signal-to-noise ratios and high cadence light curves, we need models that couple the description of stellar variability with the transits analysis, like GPs. Moreover, for Earth-like exoplanets a better description of stellar variability (achieved using multi-component models) improves the planetary characterisation. Our results are particularly important for the analysis of TESS, CHEOPS, and PLATO light curves.

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 mainsequence 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 modeled alongside the transit. With the promise of TESS delivering on the order of ∼ 10 5 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 ν max , 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, R p /R . The method's implementation is publicly available †.