Bibliographic record
Abstract
Strong encounters between single stars and binaries play a pivotal role in the evolution of star clusters. Such encounters can also dramatically modify the orbital parameters of binaries, exchange partners in and out of binaries, and are a primary contributor to the rate of physical stellar collisions in star clusters. Often, these encounters are studied under the approximation that they happen quickly enough and within a small enough volume to be considered isolated from the rest of the cluster. In this paper, we study the validity of this assumption through the analysis of a large grid of single–binary and binary–binary scattering experiments. For each encounter we evaluate the encounter duration, and compare this with the expected time until another single or binary star will join the encounter. We find that for lower-mass clusters, similar to typical open clusters in our Galaxy, the percent of encounters that will be "interrupted" by an interloping star or binary may be 20%–40% (or higher) in the core, though for typical globular clusters we expect ≲1% of encounters to be interrupted. Thus, the assumption that strong encounters occur in relative isolation breaks down for certain clusters. Instead, many strong encounters develop into more complex "mini-clusters," which must be accounted for in studying, for example, the internal dynamics of star clusters, and the physical stellar collision rate.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".