HST/STIS spectroscopy of the magnetic Of?p star HD 108: the low state at ultraviolet wavelengths
Bibliographic record
Abstract
We present the first ultraviolet spectrum of the peculiar, magnetic Of?p star HD 108 obtained in its spectroscopic low state. The new data, obtained with the Space Telescope Imaging Spectrograph (STIS) on the Hubble Space Telescope, reveal significant changes compared to IUE spectra obtained in the high state: N vλ1240, Si ivλ1400 and C ivλ1550 present weaker P Cygni profiles (less absorption) in the new data, while N ivλ1718 absorption is deeper, without the clear wind signature evident in the high state. Such changes contrast with those found in other magnetic massive stars, where more absorption is observed in the resonance doublets when the sightline is close to the plane of the magnetic equator. The new data show also that the photospheric Fe iv forest, at ∼1600–1700 Å, has strengthened compared to previous observations. The ultraviolet variability is large compared to that found in typical, non-magnetic O stars, but moderate when compared to the high-/low-state changes reported in the optical spectrum of HD 108 over several decades. We use non-local thermodynamic equilibrium (non-LTE) expanding-atmosphere models to analyse the new STIS observations. Overall, the results are in accord with a scenario in which the optical variability is mainly produced by magnetically constrained gas, close to the photosphere. The relatively modest changes found in the main ultraviolet wind lines suggest that the stellar wind is not substantially variable on a global scale. Nonetheless, multidimensional radiative-transfer models may be needed to understand some of the phenomena observed.
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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 | 0.000 | 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".