The ejection and detectability of high- and hyper-velocity stars by compact object binaries in globular clusters
Bibliographic record
Abstract
The dense cores of Milky Way globular clusters (GCs) play host to a variety of dynamical encounters between stellar objects, which can accelerate stars to velocities high enough to escape the GC. The most extreme examples of these encounters are interactions between single GC stars and binaries including at least one compact object. These interactions can result in ejection velocities of up to several hundred $\mathrm{km \ s^{-1}}$, approaching or even exceeding the escape velocity of the Galaxy itself. In order to study whether these interactions contribute to the Galactic population of hypervelocity stars (stars moving faster than the Galactic escape speed), we combine Monte Carlo $N$-body GC simulations, observations of Galactic GCs, and a particle spray code to generate realistic populations of stars which have escaped from Milky Way GCs following star + compact object binary (S+COB) interactions. We find that over the last 500 Myr, S+COB interactions have likely ejected $\sim$6300 stars from Galactic GCs, of which $839_{-67}^{+70}$ have present-day velocities exceeding $500 \; \mathrm{km \ s^{-1}}$. Using mock photometric observations, we find that $290_{-23}^{+28}$ ejected stars are detectable in Gaia Data Release 3, however, only $1_{-1}^{+2}$ stars faster than $500 \; \mathrm{km \ s^{-1}}$ are detectable. Even so, we show that observational prospects in the upcoming Legacy Survey of Space and Time are more optimistic, and future detected fast extratidal GC stars will serve as a useful probe of GC cores.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".