A systematic method to identify runaways from star clusters produced from single-binary interactions
Bibliographic record
Abstract
Context. One hypothesis for runaway stars (RSs) is that they are ejected from star clusters with high velocities relative to the cluster center-of-mass motion. There are two competing mechanisms for their production: supernova-based ejections in binaries, where one companion explodes, leaves no remnant, and launches the other companion at the instantaneous orbital velocity, and the disintegration of triples (or higher-order multiples), which produces a recoiled runaway binary (RB) and an RS. Aims. We search for RS candidates using data from the Gaia DR3 survey with a focus on triple disintegration since in this case the product is always a binary and a single star that should be moving in opposite directions. Methods. We created a systematic methodology to look for candidate RS-RB runaway pairs produced from the disintegration of bound three-body systems formed from single-binary interactions based on momentum conservation and causality. The method we use is general and can be applied to any cluster with a 5D kinematic data set. We used our criteria to search for these pairs in a 150 pc circular field of view surrounding the open cluster M67, which we used as a benchmark cluster to test the robustness of our method. Results. Our results reveal only one RS-RB pair that is consistent with all of our selection criteria out of an initial sample of 108 pairs.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".