Outflows and Jets from Collapsing Magnetized Cloud Cores
Bibliographic record
Abstract
Star formation is usually accompanied by outflow phenomena. There is strong evidence that these outflows and jets are launched from protostellar disks by magnetorotational processes. Here we report on our three-dimensional, adaptive mesh, magnetohydrodynamic simulations of collapsing, rotating, magnetized Bonnor-Ebert spheres, whose properties are taken directly from observations. In contrast to the pure hydro case, in which no outflows are seen, our present simulations show an outflow from the protodisk surface at ~130 AU and a jet at ~0.07 AU after a strong toroidal magnetic field buildup. The large-scale outflow, which extends up to ~600 AU at the end of our simulation, is driven by toroidal magnetic pressure (spring), whereas the jet is powered by magnetocentrifugal force (fling). At the final stage of our simulation these winds are still confined within two respective shock fronts. Furthermore, we find that the jet-wind and the disk-anchored magnetic field extract a considerable amount of angular momentum from the protostellar disk. The initial spin of our cloud core was chosen high enough to produce a binary system. We indeed find a close binary system (separation ~3 R ☉ ), which results from the fragmentation of an earlier formed ring structure. The magnetic field strength in these protostars reaches ~3 kG and becomes about 3 G at 1 AU from the center, in agreement with recent observational results.
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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 | 0.001 | 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".