The Impact of the Galactic Bar and the Large Magellanic Cloud on Hypervelocity Star Trajectories
Bibliographic record
Abstract
Abstract Hypervelocity stars (HVSs) ejected from the Galactic center at speeds faster than the Galactic escape velocity are useful tools to provide insight into the Milky Way’s dark matter halo. However, most characterizations of HVS orbits assume static models of the Milky Way’s gravitational potential. In this work, we assess the influence of the Galactic bar and the Large Magellanic Cloud (LMC) on HVS trajectories, comparing them with those from an axisymmetric potential. We simulate 28,000 HVSs ejected over the last 100 Myr and find that ignoring the bar and LMC can cause their apparent ejection location to drift by up to 100 pc. Applying two standard HVS potential fitting methods to our sample shows that they are unable to perform as designed when nonaxisymmetric effects are neglected. We calculate the angle between HVS Galactocentric position and velocity, and find the LMC and bar can induce a deflection angle of up to several degrees. Using mock Gaia Data Release 4 observations, however, we show that this deflection is too small in magnitude to be measured in the near future without significantly improved observational uncertainties, particularly in heliocentric distance. Our results emphasize the need to account for the bar and LMC in modeling the Galactic potential using HVSs as a tracer.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".