The Origin of B-type Runaway Stars: Non-LTE Abundances as a Diagnostic
Bibliographic record
Abstract
Abstract There are two accepted mechanisms to explain the origin of runaway OB-type stars: the binary supernova (SN) scenario and the cluster ejection scenario. In the former, an SN explosion within a close binary ejects the secondary star, while in the latter close multibody interactions in a dense cluster cause one or more of the stars to be ejected from the region at high velocity. Both mechanisms have the potential to affect the surface composition of the runaway star. tlusty non-LTE model atmosphere calculations have been used to determine the atmospheric parameters and the C, N, Mg, and Si abundances for a sample of B-type runaways. These same analytical tools were used by Hunter et al. for their analysis of 50 B-type open-cluster Galactic stars (i.e., nonrunaways). Effective temperatures were deduced using the Si-ionization balance technique, surface gravities from Balmer line profiles, and microturbulent velocities derived using the Si spectrum. The runaways show no obvious abundance anomalies when compared with stars in the open clusters. The runaways do show a spread in composition that almost certainly reflects the Galactic abundance gradient and a range in the birthplaces of the runaways in the Galactic disk. Since the observed Galactic abundance gradients of C, N, Mg, and Si are of a similar magnitude, the abundance ratios (e.g., N/Mg) are as obtained essentially uniform across the sample.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".