Benchmark Asteroseismic Masses and Ages of Red Giant Stars with TESS

Introduction •Asteroseismology provides precise age constraints of stochastically oscillating stars, such as red giants. •Age determinations for stars in different kinematic components of the Milky Way are vital for constraining the timing of merger events in the Galaxy’s past. •Most previous analyses of red giants leverage the empirically determined global asteroseismic scaling relations, which are scaled to the Sun. Typical precision for scaling-relation-based ages are between 25% and 30% (Grunblatt+ ApJ, 2021). •Grunblatt+ (ApJ, 2021) analyzed a sample of red giants in the thin disk, thick disk, in situ halo, and Gaia-Enceladus stellar populations using asteroseismic data from TESS and the asteroseismic scaling relations. •Previous work indicates using scaling relations instead of individual mode frequencies results in higher stellar mass estimates and lower inferred stellar ages. Oscillation Mode Frequency Determination •With a wealth of recent TESS data, we combine multiple sectors of data for multiple stars previously analyzed with only global seismic parameters in Grunblatt+ (ApJ, 2021). •Noise-corrected lightcurves are generated using the giants pipeline described in Saunders+ (AJ, 2022). •Individual ℓ = 0, 1, and 2 mode frequencies are determined using the peak-bagging tool TACO (Themeßl+, 2020). Individual frequency modeling of red giant stars provides more robust age constraints than global methods on the timing of most important events in the formation of the Milky Way. (Poster #117!) Benchmark Asteroseismic Masses and Ages of Red Giant Stars with TESS Samuel K. Grunblatt, Christopher J. Lindsay Modeling Methods •For each target star, we calculate MESA stellar models, varying the initial mass, helium abundance, metallicity, and mixing length. The model spectroscopic observables (Teff, Luminosity, and [Fe/H]) are compared with the data along with the GYRE-calculated ℓ = 0, 1, and 2 mode frequencies to determine the quality of a model’s fit. •The best-fit model for each target is found with an optimization procedure which employs the differential evolution algorithm from P. Mier (2017). Results •We compare the individual frequency modeling results to results derived from global asteroseismology using different established codes such as isochrones (Morton, 2015), isoclassify (Huber+, 2017, Berger+, 2020, 2023), and modelflows (Hon+, 2024). These codes rely on grids of precalculated stellar models and compare model values to the observed spectroscopic and global asteroseismic values. •Our preliminary results indicate that using only global asteroseismic observables results in higher stellar masses and lower stellar ages, in agreement with previous work. •New revised age determinations for stars in different kinematic components of the Galaxy place new constraints on the age of the Gaia-Enceladus merger event and provide benchmarks for the distribution of stellar ages within different Galactic substructures. 12 21 Methods Example (TIC 300938910) Sample of TESS Stars Determining the Mode Frequencies, νmax, and Δν Asteroseismic Modeling Comparing the Best-Fit Models with Results from Codes Incorporating Just the Global Asteroseismic Parameters See S. Sharma+ (ApJ, 2016), Y. Li+ (ApJ, 2024), D. Huber+ (ApJ, 2024), and J. Larsen+ (A&A, 2025) for additional studies of low metallicity stars and the numax scaling relation. •Aperture photometry by sector •Sector-level detrending •Global light curve stitching and detrending First ascent RGB stars, with non-disklike kinematics (aside from disk reference star). Creating the Light-curves Global asteroseismic parameters and ~20 individual modes identified by TACO. Individual modes corrected and compared to GYRE frequencies derived from MESA models. Best-fit model constrains stellar mass, He abundance, metallicity and mixing length. Strong agreement overall. Global model overestimates stellar mass by ~10%, thus underestimating age by ~50%, as predicted!