The formation and evolution of protostellar discs; three-dimensional adaptive mesh refinement hydrosimulations of collapsing, rotating Bonnor-Ebert spheres
Bibliographic record
Abstract
We present a detailed study of the collapse of molecular cloud cores using high‐resolution three‐dimensional adaptive mesh refinement (AMR) numerical simulations. In this first in a series of investigations our initial conditions consist of a spherical molecular core obeying the hydrostatic Bonnor–Ebert profile with varying degrees of initial rotation. Our simulations cover both the formation of massive discs, in which massive stars form, and low‐mass discs. We use a customized version of the flash code the AMR technique, which allows us to follow the formation of a protostellar disc and protostellar core(s) through more than 10 orders in density increase, while continuously resolving the local Jeans length (i.e. obeying the Truelove criterion). Our numerical simulations also incorporate the energy loss due to molecular line emission in order to obtain a more realistic picture of the protostellar core and disc formation. Our initial states model systems of mass 168 and 2.1 M⊙ that will form high‐ and low‐mass stars, respectively. We follow many features such as the development complex shock structures, and the fragmentation of the disc. We find that slowly rotating cores (Ω tff= 0.1) produce discs in which a strong bar develops but does not fragment. Faster initial rotation rates (Ω tff= 0.2) result in the formation of a ring, which may fragment into two protostellar cores. The size of the rings found in our simulated discs agree with the observations of similar systems.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".