Simulating protostellar evolution and radiative feedback in the cluster environment
Bibliographic record
Abstract
Radiative feedback is among the most important consequences of clustered star formation inside molecular clouds. At the onset of star formation, radiation from massive stars heats the surrounding gas, which suppresses the formation of many low‐mass stars. When simulating pre‐main‐sequence stars, their stellar properties must be defined by a pre‐stellar model. Different approaches to pre‐stellar modelling may yield quantitatively different results. In this paper, we compare two existing pre‐stellar models under identical initial conditions to gauge whether the choice of model has any significant effects on the final population of stars. The first model treats stellar radii and luminosities with a zero‐age main‐sequence (ZAMS) model, while separately estimating the accretion luminosity by interpolating to published pre‐stellar tracks. The second, more accurate pre‐stellar model self‐consistently evolves the radius and luminosity of each star under highly variable accretion conditions. Each is coupled to a raytracing‐based radiative feedback code that also treats ionization. The impact of the self‐consistent model is less ionizing radiation and less heating during the early stages of star formation. This may affect final mass distributions. We noted a peak stellar mass reduced by 8 per cent from 47.3 to 43.5 M⊙ in the evolutionary model, relative to the track‐fit model. Also, the difference in mass between the two largest stars in each case is reduced from 14 to 7.5 M⊙. The H ii regions produced by these massive stars were also seen to flicker on time‐scales down to the limit imposed by our time‐step (<560 yr), rapidly changing in size and shape, confirming previous cluster simulations using ZAMS‐based estimates for pre‐stellar ionizing flux.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".