Magnetic field, chemical composition and line profile variability of the peculiar eclipsing binary star AR Aur⋆
Bibliographic record
Abstract
AR Aur is the only eclipsing binary known to contain a HgMn star, making it an ideal case for a detailed study of the HgMn phenomenon. HgMn stars are a poorly understood class of chemically peculiar stars, which have traditionally been thought not to possess significant magnetic fields. However, the recent discovery of line profile variability in some HgMn stars, apparently attributable to surface abundance patches, has brought this belief into question. In this paper we investigate the chemical abun-dances, line profile variability, and magnetic field of the primary and secondary of the AR Aur system, using a series of high resolution spectropolarimetric observations. We find the primary is indeed a HgMn star, and present the most precise abundances yet determined for this star. We find the secondary is a weak Am star, and is possibly still on the pre-main sequence. Line profile variability was observed in a range of lines in the primary, and is attributed to inhomogeneous surface distributions of some el-ements. No magnetic field was detected in any observation of either stars, with an upper limit on the longitudinal magnetic field in both stars of 100 G. Modeling of the phase-resolve longitudinal field measurements leads to a 3σ upper limit on any dipole surface magnetic field of about 400 G.
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".