The Formation of Star Clusters II: 3D Simulations of Magnetohydrodynamic Turbulence in Molecular Clouds
Bibliographic record
Abstract
We present a series of turbulence simulations that represent a cluster-forming clump within a molecular cloud, investigating the role of magnetic fields on the formation of potential star-forming cores. We present an exhaustive analysis of numerical data from these simulations that includes the distributions of core masses, radii, mean density, angular momenta, spins and magnetizations. The simulations range between 5-30 Jeans masses of gas, and are representative of molecular cloud clumps with masses between 100-1000 M_sun. The field strengths in the bound cores that form tend to have the same ratio of gas pressure to magnetic pressure, beta, as the mean beta of the simulation. Thus, in order to explain the large magnetizations seen in Zeeman measurements of molecular cloud cores, a significant number of initial Jeans masses is needed in order to ensure that the simulations are sufficiently supercritical. Clouds that are only slightly supercritical will instead collapse along the field lines into sheets, and the cores that form as these sheets fragment have a different distribution of masses than what is observed. The spin rates of these cores (wherein 20-40% of cores have Omega t_ff >= 0.2) suggests that subsequent fragmentation into multiple systems is likely. The sizes of the bound cores that are produced are typically 0.02-0.2 pc and have densities in the range 10^4-10^5 cm^{-3} in agreement with observational surveys.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".