Dynamics of Star Cluster Formation: The Effects of Ongoing Star Formation and Stellar Feedback
Bibliographic record
Abstract
Abstract We perform a high-resolution zoomed-in simulation of star cluster assembly including the merger of two subclusters with initial conditions taken from previous large-scale giant molecular cloud (GMC) simulations. We couple hydrodynamics to N-body dynamics to simulate the individual stars themselves, and the gas-rich environment in which they evolve. We include prescriptions for star formation and stellar feedback and compare them directly to previous simulations of the same region without these prescriptions to determine their role in shaping the dynamics inherited from the cluster assembly process. The stellar mass of the cluster grows through star formation within the cluster and accretion of new stars and star-forming gas from a nearby filament. This growth results in an enhancement in the cluster’s rotation and anisotropic expansion compared to simulations without star formation. We also analyze the internal kinematics of the cluster once it has lost most of its gas and find that the rotational velocity and the velocity anisotropy profiles are qualitatively similar to those expected of clusters that have undergone violent relaxation. Additionally, rotation and anisotropic expansion are still present by the time of gas removal. This implies that evolution within the GMC was unable to completely erase the kinematics inherited by the merger.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 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".