TESS photometry of the Of?p stars HD 148937 and LMC 164-2
Bibliographic record
Abstract
Recent spectropolarimetric surveys have identified a subpopulation of O stars (<10%) that possess strong (0.1-20 kG), typically dipolar magnetic fields. These fields channel the stellar wind, which results in a dense magnetosphere that co-rotates with the star. As a result, magnetic O stars show spectral and photometric variability that is modulated by rotation. With new observations provided by the Transiting Exoplanet Survey Satellite (TESS), we can now study magnetic O-star variability at the sub-mmag level. In addition, the large sky coverage provided by TESS offers the potential to identify new candidate magnetic O stars for follow-up spectropolarimetric observations. Careful analysis of known magnetic O star TESS photometry is essential to understanding how to identify rotational modulation in the larger TESS O star sample. Here we discuss the TESS photometry of HD 148937, one of the few magnetic O stars with a reported rotational period (Prot=7.03 d) short enough to be detected in a single sector (~27 d). Additionally, we show analysis of the TESS photometry of the short period magnetic candidate Of?p stars in the Magellanic Clouds, which have been observed over many sectors. We discuss issues related to detrending and contamination in the TESS pipeline processed light curves and the subsequent impact on our efforts to identify new candidate magnetic O 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 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) | 0.002 | 0.001 |
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".