Revisiting the pulsational characteristics of the exoplanet host star<i>β</i>Pictoris
Bibliographic record
Abstract
Context.Exoplanet properties crucially depend on the parameters of their host stars: more accurate stellar parameters yield more accurate exoplanet characteristics. When the exoplanet host star shows pulsations, asteroseismology can be used for an improved description of the stellar parameters. Aims.We aim to revisit the pulsational properties ofβPic and identify its pulsation modes from normalized amplitudes in five different passbands. We also investigate the potential presence of a magnetic field. Methods.We conducted a frequency analysis using three seasons of BRITE-Constellation observations in the two BRITE filters, the about 620-day-long bRing light curve, and the nearly 8-year-long SMEI photometric time series. We calculated normalized amplitudes using all passbands and including previously published values obtained from ASTEP observations. We investigated the magnetic properties ofβPic using spectropolarimetric observations conducted with the HARPSpol instrument. Using 2D rotating models, we fit the normalized amplitudes and frequencies through Monte Carlo Markov chains. Results.We identify 15 pulsation frequencies in the range from 34 to 55 d−1, where two, F13 at 53.6917 d−1and F11 at 50.4921 d−1, display clear amplitude variability. We use the normalized amplitudes in up to five passbands to identify the modes as three ℓ = 1, six ℓ = 2, and six ℓ = 3 modes.βPic is shown to be non-magnetic with an upper limit of the possible undetected dipolar field of 300 Gauss. Conclusions.Multiple fits to the frequencies and normalized amplitudes are obtained, including one with a near equator-on inclination forβPic, which corresponds to our expectations based on the orbital inclination ofβPic b and the orientation of the circumstellar disk. This solution leads to a rotation rate of 27% of the Keplerian breakup velocity, a radius of 1.497 ± 0.025R⊙, and a mass of 1.797 ± 0.035M⊙. The ∼2% errors in radius and mass do not account for uncertainties in the models and a potentially erroneous mode-identification.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".