Stellar Encounters with Multiple Star Systems and the Blue Straggler Problem
Bibliographic record
Abstract
We present a technique to identify the most probable dynamical formation scenario for an observed binary or triple system containing one or more merger products or, alternatively, to rule out the possibility of a dynamical origin. Our method relies on an analytic prescription for energy conservation during stellar encounters. With this, observations of the multiple star system containing the merger product(s) can be used to work backwards in order to constrain the initial orbital energies of any single, binary or triple systems that went into the encounter. The initial semi‐major axes of the orbits provide an estimate for the collisional cross section and therefore the time‐scale for the encounter to occur in its host cluster.We have applied our analytic prescription to observed binary and triple systems containing blue stragglers, in particular the triple system S1082 in M67 and the period distribution of the blue straggler binaries in NGC 188. We have shown that both S1082 and most of the blue straggler binaries in NGC 188 could have a dynamical origin, and that encounters involving triples are a significant contributor to blue straggler populations in old open clusters. In general, our results suggest that encounters involving triples could make up a significant fraction of those dynamical interactions that result in stellar mergers, in particular encounters that produce multiple‐star systems containing one or more blue stragglers.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".