Rotation Signatures of TESS B-type Stars: Enlarging the Sample
Bibliographic record
Abstract
Abstract Massive stars are essential for the evolution and chemical enrichment of the universe, yet their structure and evolution remain poorly understood. This study aims to expand the sample of B-type stars with known rotation periods by analyzing NASA’s Transiting Exoplanet Survey Satellite (TESS) light curves (LCs). The analysis encompasses 373 B-type stars observed with 2 minute cadence LCs from TESS, employing a manifold approach that integrates three techniques: the Fast Fourier Transform, the Lomb–Scargle periodogram, and wavelet analysis. Rotational periods were identified for 14 new B-type stars in the TESS data, while periods for 16 previously studied targets were confirmed based on literature data. Among the remaining 343 stars, as a byproduct of our analysis, we have identified 36 pulsating candidates, seven with binary signatures, and 48 hot subdwarf (sdB) candidates. Integrating these three techniques offers a robust method for separating stellar rotation from other sources of variability in the LCs, such as pulsation, binarity, and sdB. Finally, the rotational periodicities identified in this study could provide valuable constraints for refining stellar evolution models, particularly those that include rotation and advancing asteroseismic analyses of massive stars.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".