The Star Clusters as Links between Galaxy Evolution and Star Formation Project. I. Numerical Method
Bibliographic record
Abstract
Abstract Stellar clusters are critical constituents within galaxies: They are the result of highest-density star formation, and through their spatially and temporally correlated feedback, they regulate their host galaxy evolution. We present a novel numerical method to model star clusters as individual units of star formation using sink particles. In our method, star clusters grow via gas accretion and via merging with less massive clusters. We describe the implementation in the radiation hydrodynamics code GIZMO and run a large grid of marginally bound, turbulent clouds of 107 M ⊙ to explore the effect of modeling ingredients on the evolution of the clouds and the star clusters. We find both gas accretion and mergers to be critical processes to form star clusters of masses up to ∼105–106 M ⊙, while ionizing radiation is the main feedback mechanism regulating the growth of star clusters. The majority of our star clusters assemble their mass in 0.3–2.6 Myr, and the most massive ones take ∼10 Myr. By removing high-density gas by accretion, our sink-based cluster formation prescription allows the newly formed star clusters to inject their stellar feedback in less dense environments. This makes feedback more efficient at ionizing and disrupting the cloud than if we were to use a standard star formation approach, indicating that our numerical method is the missing critical step to model the interplay between star clusters and their host galaxies.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".