THE ROLE OF TURBULENCE AND MAGNETIC FIELDS IN SIMULATED FILAMENTARY STRUCTURE
Bibliographic record
Abstract
We use numerical simulations of turbulent cluster-forming regions to study the nature of dense filamentary structures in star formation. Using four hydrodynamic and magnetohydrodynamic simulations chosen to match observations, we identify filaments in the resulting column density maps and analyze their properties. We calculate the radial column density profiles of the filaments every 0.05 Myr and fit the profiles with the modified isothermal and pressure-confined isothermal cylinder models, finding reasonable fits for either model. The filaments formed in the simulations have similar radial column density profiles to those observed. Magnetic fields provide additional pressure support to the filaments, making "puffier" filaments less prone to fragmentation than in the pure hydrodynamic case, which continue to condense at a slower rate. In the higher density simulations, the filaments grow faster through the increased importance of gravity. Not all of the filaments identified in the simulations will evolve to form stars: some expand and disperse. Given these different filament evolutionary paths, the trends in bulk filament width as a function of time, magnetic field strength, or density are weak, and all cases are reasonably consistent with the finding of a constant filament width in different star-forming regions. In the simulations, the mean FWHM lies between 0.06 and 0.26 pc for all times and initial conditions, with most lying between 0.1 to 0.15 pc; the range in FWHMs is however, larger than seen in typical Herschel analyses. Finally, the filaments display a wealth of substructure similar to the recent discovery of filament bundles in Taurus.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".