Cluster formation in molecular clouds – I. Stellar populations, star formation rates and ionizing radiation
Bibliographic record
Abstract
We present a model for the radiative output of star clusters in the process of star formation suitable for use in hydrodynamical simulations of radiative feedback. Gas in a clump, defined as a region whose density exceeds 104 cm−3, is converted to stars via the random sampling of the Chabrier initial mass function. A star formation efficiency controls the rate of star formation. We have completed a suite of simulations which follow the evolution of accretion-fed clumps with initial masses ranging from 0 to 105 M⊙ and accretion rates ranging from 10−5 to 10−1 M⊙ yr−1. The stellar content is tracked over time which allows the aggregate luminosity, ionizing photon rate, number of stars and star formation rate (SFR) to be determined. For a fiducial clump of 104 M⊙, the luminosity is ∼4 × 106 L⊙ with an SFR of roughly 3 × 10−3 M⊙ yr−1. We identify two regimes in our model. The accretion-dominated regime obtains the majority of its gas through accretion and is characterized by an increasing SFR, while the reservoir-dominated regime has the majority of its mass present in the initial clump with a decreasing SFR. We show that our model can reproduce the expected number of O stars, which dominate the radiative output of the cluster. We find a nearly linear relationship between SFR and mass as seen in observations. We conclude that our model is an accurate and straightforward way to represent the output of clusters in hydrodynamical simulations with radiative feedback.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".