New insights on stellar evolution and stellar interior physics are being made possible by asteroseismology. Throughout the course of the Kepler mission, asteroseismology has also played an important role in the characterization of exoplanet-host stars and their planetary systems. The upcoming NASA Transiting Exoplanet Survey Satellite (TESS) will be performing a near all-sky survey for planets that transit bright nearby stars. In addition, its excellent photometric precision, combined with its fine time sampling and long intervals of uninterrupted observations, will enable asteroseismology of solar-type and red-giant stars. Here we develop a simple test to estimate the detectability of solar-like oscillations in TESS photometry of any given star. Based on an all-sky stellar and planetary synthetic population, we go on to predict the asteroseismic yield of the TESS mission, placing emphasis on the yield of exoplanet-host stars for which we expect to detect solar-like oscillations. This is done for both the target stars (observed at a 2-minute cadence) and the full-frame-image stars (observed at a 30-minute cadence). A similar exercise is also conducted based on a compilation of known host stars. We predict that TESS will detect solar-like oscillations in a few dozen target hosts (mainly subgiant stars but also in a smaller number of F dwarfs), in up to 200 low-luminosity red-giant hosts, and in over 100 solar-type and red-giant known hosts, thereby leading to a threefold improvement in the asteroseismic yield of exoplanet-host stars when compared to Keplerʼs.

Asteroseismic constraints on K giants make it possible to infer radii, masses and ages of tens of thousands of field stars. Tests against independent estimates of these properties are however scarce, especially in the metal-poor regime. Here, we report the detection of solar-like oscillations in 8 stars belonging to the red-giant branch and red-horizontal branch of the globular cluster M4. The detections were made in photometric observations from the K2 Mission during its Campaign 2. Making use of independent constraints on the distance, we estimate masses of the 8 stars by utilising different combinations of seismic and non-seismic inputs. When introducing a correction to the ∆ν scaling relation as suggested by stellar models, for RGB stars we find excellent agreement with the expected masses from isochrone fitting, and with a distance modulus derived using independent methods. The offset with respect to independent masses is lower, or comparable with, the uncertainties on the average RGB mass (4 − 10%, depending on the combination of constraints used). Our results lend confidence to asteroseismic masses in the metal poor regime. We note that a larger sample will be needed to allow more stringent tests to be made of systematic uncertainties in all the observables (both seismic and non-seismic), and to explore the properties of RHB stars, and of different populations in the cluster.

A key aspect in the determination of stellar properties is the comparison of observational constraints with predictions from stellar models. Asteroseismic Inference on a Massive Scale (AIMS) is an open source code that uses Bayesian statistics and a Markov Chain Monte Carlo approach to find a representative set of models that reproduce a given set of classical and asteroseismic constraints. These models are obtained by interpolation on a pre-calculated grid, thereby increasing computational efficiency. We test the accuracy of the different operational modes within AIMS for grids of stellar models computed with the Liège stellar evolution code (main sequence and red giants) and compare the results to those from another asteroseismic analysis pipeline, PARAM. Moreover, using artificial inputs generated from models within the grid (assuming the models to be correct), we focus on the impact on the precision of the code when considering different combinations of observational constraints (individual mode frequencies, period spacings, parallaxes, photospheric constraints,...). Our tests show the absolute limitations of precision on parameter inferences using synthetic data with AIMS, and the consistency of the code with expected parameter uncertainty distributions. Interpolation testing highlights the significance of the underlying physics to the analysis performance of AIMS and provides caution as to the upper limits in parameter step size. All tests demonstrate the flexibility and capability of AIMS as an analysis tool and its potential to perform accurate ensemble analysis with current and future asteroseismic data yields.