Constraints on the Galactic Centre environment from <i>Gaia</i> hyper-velocity stars
Bibliographic record
Abstract
ABSTRACT Following a dynamical encounter with Sgr A*, binaries in the Galactic Centre (GC) can be tidally separated and one member star ejected as a hyper-velocity star (HVS) with a velocity beyond the escape speed of the Milky Way. As GC-born objects located in more observationally accessible regions of the sky, HVSs offer insight into the stellar population in the inner parsecs of the Milky Way. We perform a suite of simulations ejecting stars from the GC, exploring how detectable HVS populations depend on assumptions concerning the GC stellar population, focusing on HVSs that would appear in current and/or future data releases from the Gaia space mission with precise astrometry and measured radial velocities. We show that predictions are sensitive to two parameters in particular: the shape of the stellar initial mass function (IMF) in the GC and the ejection rate of HVSs. The absence of confident HVS candidates in Gaia Data Release 2 excludes scenarios in which the HVS ejection rate is $\gtrsim 3\times 10^{-2} \, \mathrm{yr^{-1}}$. Stricter constraints will be placed on these parameters when more HVS candidates are unearthed in future Gaia data releases – assuming recent determinations of the GC IMF shape, one confident HVS at minimum is expected in Gaia DR3 and DR4 as long as the HVS ejection rate is greater than $\sim 10^{-3}$ and $\sim 10^{-5} \, \mathrm{yr^{-1}}$, respectively.
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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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".