Exploring the impact of a decelerating bar on transforming bulge orbits into disc-like orbits
Bibliographic record
Abstract
Aims. The most metal-poor tail of the Milky Way ([Fe/H] ≤ −2.5) contains a population of stars on very prograde planar orbits, whose origins and evolution remain puzzling. One possible scenario is that they are shepherded by the bar from the inner Galaxy, where many of the old and low-metallicity stars in the Galaxy are located. Methods. To investigate this scenario, we used test-particle simulations with an axisymmetric background potential plus a central bar model. The test particles were generated by an extended distribution function (EDF) model based on the observational constraints of bulge stars. Results. According to the simulation results, a bar with a constant pattern speed is not efficient in terms of helping bring stars from the bulge to the solar vicinity. In contrast, when the model includes a decelerating bar, some bulge stars can gain rotation and move outwards as they are trapped in the bar’s resonance regions. The resulting distribution of shepherded stars heavily depends on the present-day azimuthal angle between the bar and the Sun. The majority of the low-metallicity bulge stars driven outwards are distributed in the first and fourth quadrants of the Galaxy with respect to the Sun and about 10% of them are within 6 kpc from us. Conclusions. Our experiments indicate that the decelerating bar perturbation can be a contributing mechanism that may partially explain the presence of the most metal-poor stars with prograde planar orbits in the Solar neighbourhood, but it is unlikely to be the only one.
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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 | 0.002 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".