Supersonic turbulence, filamentary accretion and the rapid assembly of massive stars and discs
Bibliographic record
Abstract
We present a detailed computational study of the assembly of protostellar discs and massive stars in molecular clouds with supersonic turbulence. We follow the evolution of large-scale filamentary structures in a cluster-forming clump down to protostellar length-scales by means of very highly resolved, 3D adaptive mesh refined (AMR) simulations, and show how accretion discs and massive stars form in such environments. We find that an initially elongated cloud core which has a slight spin from oblique shocks collapses first to a filament and later develops a turbulent disc close to the centre of the filament. The continued large-scale flow that shocks with the filament maintains the high density and pressure within it. Material within the cooling filament undergoes gravitational collapse and an outside-in assembly of a massive protostar. Our simulations show that very high mass accretion rates of up to 10−2 M⊙ yr−1 and high, supersonic, infall velocities result from such filamentary accretion. Accretion at these rates is higher by an order of magnitude than those found in semi-analytic studies, and can quench the radiation field of a growing massive young star. Our simulations include a comprehensive set of the important chemical and radiative processes such as cooling by molecular line emission, gas–dust interaction and radiative diffusion in the optically thick regime, as well as H2 formation and dissociation. Therefore, we are able to probe, for the first time, the relevant physical phenomena on all scales from those characterizing the clump down to protostellar core.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".