On magnetic inhibition of photospheric macroturbulence generated in the iron-bump opacity zone of O-stars
Bibliographic record
Abstract
Massive, hot OB-stars show clear evidence of strong macroscopic broadening (in addition to rotation) in their photospheric spectral lines. This paper examines the occurrence of such ‘macroturbulence’ in slowly rotating O-stars with strong, organized surface magnetic fields. Focusing on the C iv 5811 Å line, we find evidence for significant macroturbulent broadening in all stars except NGC 1624−2, which also has (by far) the strongest magnetic field. Instead, the very sharp C iv lines in NGC 1624−2 are dominated by magnetic Zeeman broadening, from which we estimate a dipolar field ∼20 kG. By contrast, magnetic broadening is negligible in the other stars (due to their weaker field strengths, on the order of 1 kG), and their C iv profiles are typically very broad and similar to corresponding lines observed in non-magnetic O-stars. Quantifying this by an isotropic, Gaussian macroturbulence, we derive vmac = 2.2 ±|$^{0.9}_{2.2}$| km s−1 for NGC 1624 and vmac ≈ 20–65 km s−1 for the rest of the magnetic sample. We use these observational results to test the hypothesis that the field can stabilize the atmosphere and suppress the generation of macroturbulence down to stellar layers where the magnetic pressure PB and the gas pressure Pg are comparable. Using a simple grey atmosphere to estimate the temperature T0 at which PB = Pg, we find that T0 > Teff for all investigated magnetic stars, but that T0 reaches the ∼ 160 000 K layers associated with the iron opacity bump in hot stars only for NGC 1624−2. This is consistent with the view that the responsible physical mechanism for photospheric O-star macroturbulence may be stellar gravity-mode oscillations excited by sub-surface convection zones, and it suggests that a sufficiently strong magnetic field can suppress such iron-bump generated convection and associated pulsational excitation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| 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".