Comparing hypervelocity star populations from the Large Magellanic Cloud and the Milky Way
Bibliographic record
Abstract
ABSTRACT We predict and compare the distributions and properties of hypervelocity stars (HVSs) ejected from the centres of the Milky Way (MW) and the Large Magellanic Cloud (LMC). In our model, HVSs are ejected at a constant rate – equal in both galaxies – via the Hills mechanism and are propagated in a combined potential, where the LMC orbits the MW on its first infall. By selecting $m \gt 2\, \mathrm{M_\odot }$ HVSs well separated from the Magellanic Clouds and Galactic mid-plane, we identify mock HVSs which would stand out from ordinary stars in the stellar halo in future data releases from the Gaia satellite and the Vera C. Rubin Observatory’s Legacy Survey of Space and Time (LSST). We find that in these deep surveys, LMC HVSs will outnumber MW ones by a factor of ∼2.5, as HVSs can more easily escape from the shallower potential of the LMC. At an assumed HVS ejection rate of $10^{-4} \, \mathrm{yr^{-1}}$, HVSs detectable in the final Gaia data release and LSST from the LMC (MW) will number $125_{-12}^{+11}$ ($50_{-8}^{+7}$) and $140_{-11}^{+10}$ ($42_{-7}^{+6}$), respectively. The MW and LMC HVS populations show different kinematics and spatial distributions. While LMC HVSs have more modest total velocities and larger Galactocentric distances clustered around those of the LMC itself, HVSs from the MW show broader distributions, including a prominent high-velocity tail above $500 \, \mathrm{km \ s^{-1}}$ that contains at least half of the stars. These predictions are robust against reasonable variation of the Galactic potential and of the LMC central black hole mass.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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".