Constraints on the Galactic Centre environment from <i>Gaia</i> hypervelocity stars II: The evolved population
Bibliographic record
Abstract
ABSTRACT A dynamical encounter between a stellar binary and Sgr A* in the Galactic Centre (GC) can tidally separate the binary and eject one member with a velocity beyond the escape speed of the Milky Way. These hypervelocity stars (HVSs) can offer insight into the stellar populations in the GC environment. In a previous work, our simulations showed that the lack of main sequence HVS candidates with precise astrometric uncertainties and radial velocities in current data releases from the Gaia space mission places a robust upper limit on the ejection rate of HVSs from the GC of $3\times 10^{-2} \, \mathrm{yr^{-1}}$. We improve this constraint in this work by additionally considering the absence of post-main sequence HVSs in Gaia Early Data Release 3 as well as the existence of the HVS candidate S5-HVS1. This evidence offers degenerate joint constraints on the HVS ejection rate and the stellar initial mass function (IMF) in the GC. For a top-heavy GC IMF as suggested by recent works, our modelling motivates an HVS ejection rate of $\eta =0.7_{-0.5}^{+1.5} \times 10^{-4} \, \mathrm{yr^{-1}}$. This preferred ejection rate can be as large as $10^{-2} \, \mathrm{yr^{-1}}$ for a very top-light IMF and as low as 10$^{-4.5} \, \mathrm{yr^{-1}}$ if the IMF is extremely top-heavy. Constraints will improve further with future Gaia data releases, regardless of how many HVS candidates are found therewithin.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.002 | 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".