Magnetic field monitoring of four massive A-F supergiants
Bibliographic record
Abstract
We report magnetic field measurements spanning about 15 years of four massive ($7.5-15 M_\odot$) supergiant stars: $α$ Per (HD\,20902, F5Iab), $α$ Lep (HD\,36673A, F0Ib), $η$ Leo (HD\,87737, A0Ib) and 13 Mon (HD\,46300, A1Ib). For each star, spectropolarimetric observations were collected using ESPaDOnS at the Canada-France-Hawaii Telescope. The observed spectra were co-added, normalized, then processed using Least Squares Deconvolution (LSD) to yield mean Stokes $I$ and $V$ profiles. Each spectrum was analyzed to infer the False Alarm Probability of signal detection, and the longitudinal magnetic field was measured. This process yielded persistent detection of magnetic fields in all four stars. The median $1σ$ longitudinal field uncertainty of the Zeeman detections was 0.6~G. The maximum unsigned longitudinal magnetic fields measured from the detections are rather weak, ranging from $0.34\pm 0.19$ G for $α$ Lep to $2.61\pm 0.55$ G for 13 Mon. The Zeeman signatures show different levels of complexity; those of the two hotter stars are relatively simple, while those of the two cooler stars are more complex. The stars also exhibited different levels of variability of their Zeeman signatures and longitudinal fields. We report periodic variability of the longitudinal field and (complex) Stokes $V$ profiles of $α$ Per with a period of either 50.75 or 90 days. The (simple) Stokes $V$ profiles of 13~Mon, and probably those of $η$ Leo, show global polarity changes once during the period of observation, but the data are insufficient to place strong constraints on the variability timescales.
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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 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.000 | 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".