Massive Star Formation via High Accretion Rates and Early Disk‐driven Outflows
Bibliographic record
Abstract
We present an investigation of the massive star formation that results from the gravitational collapse of massive, magnetized molecular cloud cores. We investigate this by means of highly resolved, numerical simulations of initial magnetized Bonnor-Ebert-spheres that undergo collapse and cooling. By comparing three different cases—an isothermal collapse, a collapse with radiative cooling, and a magnetized collapse—we show that massive stars assemble quickly, with mass accretion rates exceeding 10 -3 M ☉ yr -1 . We confirm that the mass accretion during the collapsing phase is much more efficient than predicted by self-similar collapse solutions, i.e., ~ c 3 / G . We find that during protostellar assembly, the mass accretion reaches 20-100 times c 3 / G . Furthermore, we determined the self-consistent structure of the bipolar outflows produced in our three-dimensional magnetized collapse simulations. These outflows produce cavities out of which radiation pressure can be released, thereby reducing the limitations on the final mass of massive stars formed by gravitational collapse. Moreover, we argue that the extraction of angular momentum by disk-threaded magnetic fields and/or by the appearance of bars with spiral arms significantly enhances the mass accretion rate, thereby helping the massive protostar to assemble more quickly.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".