Dynamics of Star Cluster Formation: Mergers in Gas-rich Environments
Bibliographic record
Abstract
Abstract We perform high-resolution simulations of forming star clusters as they merge inside giant molecular clouds (GMCs) using hydrodynamics coupled to N-body dynamics to simultaneously model both the gas and stars. We zoom in to previously run GMC simulations and resolve clusters into their stellar and gas components while including the surrounding GMC environment. We find that GMC gas is important in facilitating the growth of clusters in their embedded phase by promoting cluster mergers. Mergers induce asymmetric expansion of the stellar component of the clusters in our simulations. As well, mergers induce angular momentum in the clusters’ stellar and gas components. We find that mergers can lead to an increase in the amount of dense gas present in clusters if a background gas distribution is present. We predict that this can lead to new star formation that can change the overall distribution of cluster stars in velocity space. Our results suggest that subcluster mergers in the presence of background gas can imprint dynamical signatures that can be used to constrain cluster formation histories.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 | 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".