Characterizing the high-velocity stars of RAVE: the discovery of a metal-rich halo star born in the Galactic disc
Bibliographic record
Abstract
We aim to characterize high-velocity (HiVel) stars in the solar vicinity both chemically and kinematically using the fourth data release of the RAdial Velocity Experiment (RAVE). We used a sample of 57 HiVel stars with Galactic rest-frame velocities larger than 275 km s−1. With 6D position and velocity information, we integrated the orbits of the HiVel stars and found that, on average, they reach out to 13 kpc from the Galactic plane and have relatively eccentric orbits consistent with the Galactic halo. Using the stellar parameters and [α/Fe] estimates from RAVE, we found the metallicity distribution of the HiVel stars peak at [M/H] = −1.2 dex and is chemically consistent with the inner halo. There are a few notable exceptions that include a hypervelocity star candidate, an extremely HiVel bound halo star, and one star that is kinematically consistent with the halo but chemically consistent with the disc. High-resolution spectra were obtained for the metal-rich HiVel star candidate and the second highest velocity star in the sample. Using these high-resolution data, we report the discovery of a metal-rich halo star that has likely been dynamically ejected into the halo from the Galactic thick disc. This discovery could aid in explaining the assembly of the most metal-rich component of the Galactic halo.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 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) | 0.000 | 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".