TESS Asteroseismology of α Mensae: Benchmark Ages for a G7 Dwarf and Its M Dwarf Companion

Asteroseismology of bright stars has become increasingly important as a method to determine the fundamental properties (in particular ages) of stars. The Kepler Space Telescope initiated a revolution by detecting oscillations in more than 500 main-sequence and subgiant stars. However, most Kepler stars are faint and therefore have limited constraints from independent methods such as long-baseline interferometry. Here we present the discovery of solar-like oscillations in α Men A, a naked-eye (V = 5.1) G7 dwarf in TESS’s southern continuous viewing zone. Using a combination of astrometry, spectroscopy, and asteroseismology, we precisely characterize the solar analog α Men A (T eff = 5569 ± 62 K, R ⋆ = 0.960 ± 0.016 R ⊙, M ⋆ = 0.964 ± 0.045 M ⊙). To characterize the fully convective M dwarf companion, we derive empirical relations to estimate mass, radius, and temperature given the absolute Gaia magnitude and metallicity, yielding M ⋆ = 0.169 ± 0.006 M ⊙, R ⋆ = 0.19 ± 0.01 R ⊙, and T eff = 3054 ± 44 K. Our asteroseismic age of 6.2 ± 1.4 (stat) ± 0.6 (sys) Gyr for the primary places α Men B within a small population of M dwarfs with precisely measured ages. We combined multiple ground-based spectroscopy surveys to reveal an activity cycle of P = 13.1 ± 1.1 yr for α Men A, a period similar to that observed in the Sun. We used different gyrochronology models with the asteroseismic age to estimate a rotation period of ∼30 days for the primary. Alpha Men A is now the closest (d = 10 pc) solar analog with a precise asteroseismic age from space-based photometry, making it a prime target for next-generation direct-imaging missions searching for true Earth analogs.

Mode angular degree identification in subgiant stars with convolutional neural networks based on power spectrum

ABSTRACT The identification of the angular degrees l of oscillation modes is essential for asteroseismology and it depends on visual tagging before fitting power spectra in a so-called peakbagging analysis. In oscillating subgiants, radial (l = 0) mode frequencies are distributed linearly in frequency, while non-radial (l ≥ 1) modes are p–g mixed modes that have a complex distribution in frequency that increases the difficulty of identifying l. In this study, we trained a one-dimensional convolutional neural network to perform this task using smoothed oscillation spectra. By training simulation data and fine-tuning the pre-trained network, we achieved 95 per cent accuracy for Kepler data.

The Close Binary Fraction as a Function of Stellar Parameters in APOGEE: A Strong Anti-Correlation With α Abundances

We use observations from the APOGEE survey to explore the relationship between stellar parameters and multiplicity. We combine high-resolution repeat spectroscopy for 41,363 dwarf and subgiant stars with abundance measurements from the APOGEE pipeline and distances and stellar parameters derived using Gaia DR2 parallaxes from Sanders & Das (2018) to identify and characterise stellar multiples with periods below 30 years, corresponding to ΔRVmax≳ 3 km s−1, where ΔRVmax is the maximum APOGEE-detected shift in the radial velocities. Chemical composition is responsible for most of the variation in the close binary fraction in our sample, with stellar parameters like mass and age playing a secondary role. In addition to the previously identified strong anti-correlation between the close binary fraction and [Fe/H] we find that high abundances of α elements also suppress multiplicity at most values of [Fe/H] sampled by APOGEE. The anti-correlation between α abundances and multiplicity is substantially steeper than that observed for Fe, suggesting C, O, and Si in the form of dust and ices dominate the opacity of primordial protostellar disks and their propensity for fragmentation via gravitational stability. Near [Fe/H] = 0 dex, the bias-corrected close binary fraction (a < 10 au) decreases from ≈ 100 per cent at [α/H] = −0.2 dex to ≈ 15 per cent near [α/H] = 0.08 dex, with a suggestive turn-up to ≈20 per cent near [α/H] = 0.2. We conclude that the relationship between stellar multiplicity and chemical composition for sun-like dwarf stars in the field of the Milky Way is complex, and that this complexity should be accounted for in future studies of interacting binaries.

Asteroseismic inference of subgiant evolutionary parameters with deep learning

ABSTRACT With the observations of an unprecedented number of oscillating subgiant stars expected from NASA’s TESS mission, the asteroseismic characterization of subgiant stars will be a vital task for stellar population studies and for testing our theories of stellar evolution. To determine the fundamental properties of a large sample of subgiant stars efficiently, we developed a deep learning method that estimates distributions of fundamental parameters like age and mass over a wide range of input physics by learning from a grid of stellar models varied in eight physical parameters. We applied our method to four Kepler subgiant stars and compare our results with previously determined estimates. Our results show good agreement with previous estimates for three of them (KIC 11026764, KIC 10920273, KIC 11395018). With the ability to explore a vast range of stellar parameters, we determine that the remaining star, KIC 10005473, is likely to have an age 1 Gyr younger than its previously determined estimate. Our method also estimates the efficiency of overshooting, undershooting, and microscopic diffusion processes, from which we determined that the parameters governing such processes are generally poorly constrained in subgiant models. We further demonstrate our method’s utility for ensemble asteroseismology by characterizing a sample of 30 Kepler subgiant stars, where we find a majority of our age, mass, and radius estimates agree within uncertainties from more computationally expensive grid-based modelling techniques.

A search for transiting planets around FGKM dwarfs and subgiants in the TESS full frame images of the Southern ecliptic hemisphere

ABSTRACT In this work, we present the analysis of 976 814 FGKM dwarf and subgiant stars in the Transiting Exoplanet Survey Telescope (TESS) full frame images (FFIs) of the Southern ecliptic hemisphere. We present a new pipeline, DIAmante, developed to extract optimized, multisector photometry from TESS FFIs and a classifier, based on the Random Forest technique, trained to discriminate plausible transiting planetary candidates from common false positives. A new statistical model was developed to provide the probability of correct identification of the source of variability. We restricted the planet search to the stars located in the least crowded regions of the sky and identified 396 transiting planetary candidates among which 252 are new detections. The candidates’ radius distribution ranges between 1 R⊕ and 2.6 RJ with median value of 1 RJ and the period distribution ranges between 0.25 and 105 d with median value of 3.8 d. The sample contains four long period candidates (P > 50 d), one of which is new, and 64 candidates with periods between 10 and 50 d (42 new ones). In the small planet radius domain (4R < R⊕), we found 39 candidates among which 15 are new detections. Additionally, we present 15 single transit events (14 new ones), a new candidate multiplanetary system, and a novel candidate around a known TOI. By using Gaia dynamical constraints, we found that 70 objects show evidence of binarity. We release a catalogue of the objects we analysed and the corresponding light curves and diagnostic figures through the MAST and ExoFOP portals.

Fast and Automated Peak Bagging with DIAMONDS (FAMED)

Stars of low and intermediate mass that exhibit oscillations may show tens of detectable oscillation modes each. Oscillation modes are a powerful tool to constrain the internal structure and rotational dynamics of the star, hence allowing one to obtain an accurate stellar age. The tens of thousands of solar-like oscillators that have been discovered thus far are representative of the large diversity of fundamental stellar properties and evolutionary stages available. Because of the wide range of oscillation features that can be recognized in such stars, it is particularly challenging to properly characterize the oscillation modes in detail, especially in light of large stellar samples. Overcoming this issue requires an automated approach, which has to be fast, reliable, and flexible at the same time. In addition, this approach should not only be capable of extracting the oscillation mode properties of frequency, linewidth, and amplitude from stars in different evolutionary stages, but also able to assign a correct mode identification for each of the modes extracted. Here we present the new freely available pipeline FAMED (Fast and AutoMated pEak bagging with DIAMONDS), which is capable of performing an automated and detailed asteroseismic analysis in stars ranging from the main sequence up to the core-helium-burning phase of stellar evolution. This, therefore, includes subgiant stars, stars evolving along the red giant branch (RGB), and stars likely evolving toward the early asymptotic giant branch. In this paper, we additionally show how FAMED can detect rotation from dipolar oscillation modes in main sequence, subgiant, low-luminosity RGB, and core-helium-burning stars.

Convective excitation and damping of solar-like oscillations

ABSTRACT The last decade has seen a rapid development in asteroseismology thanks to the CoRoT and Kepler missions. With more detailed asteroseismic observations available, it is becoming possible to infer exactly how oscillations are driven and dissipated in solar-type stars. We have carried out three-dimensional (3D) stellar atmosphere simulations together with one-dimensional (1D) stellar structural models of key benchmark turn-off and subgiant stars to study this problem from a theoretical perspective. Mode excitation and damping rates are extracted from 3D and 1D stellar models based on analytical expressions. Mode velocity amplitudes are determined by the balance between stochastic excitation and linear damping, which then allows the estimation of the frequency of maximum oscillation power, νmax, for the first time based on ab initio and parameter-free modelling. We have made detailed comparisons between our numerical results and observational data and achieved very encouraging agreement for all of our target stars. This opens the exciting prospect of using such realistic 3D hydrodynamical stellar models to predict solar-like oscillations across the Hertzsprung–Russell diagram, thereby enabling accurate estimates of stellar properties such as mass, radius, and age.