Ambipolar diffusion and the mass-to-flux ratio in a turbulent collapsing cloud
Bibliographic record
Abstract
Context. The formation of stars is governed by the intricate interplay of nonideal magnetohydrodynamic (MHD) effects, gravity, and turbulence. Computational challenges have hindered a comprehensive 3D exploration of this interplay, posing a longstanding challenge to our theoretical understanding of molecular clouds and cores. Aims. Our objective is to study both the spatial features and the time evolution of the neutral-ion drift velocity and the mass-to-flux ratio in a 3D chemo-dynamical simulation of a supercritical turbulent collapsing molecular cloud. Methods. Using our modified version of the FLASH astrophysical code, we performed a 3D nonideal MHD simulation of a turbulent collapsing molecular cloud. The resistivities of the cloud were computed self-consistently from a vast nonequilibrium chemical network containing 115 species. To compute the resistivities, we used different mean collisional rates for each charged species in our network. We additionally developed a new generalized method to measure the true mass-to-flux ratio in 3D simulations. Results. Despite the cloud’s turbulent nature, at early times, the neutral-ion drift velocity follows the expected structure from 2D axisymmetric nonideal MHD simulations with an hourglass magnetic field. At later times, however, the neutral-ion drift velocity becomes increasingly complex, with many vectors pointing outward from the cloud’s center. Specifically, we find that the drift velocity above and below the cloud’s “midplane” is in “antiphase”. We explain these features on the basis of magnetic helical loops and the correlation of the drift velocity with the magnetic tension force per unit volume. Despite the complex structure of the neutral-ion drift velocity, we demonstrate that, when averaged over a region, the true mass-to-flux ratio monotonically increases as a function of time and decreases as a function of the radius from the center of the cloud. In contrast, the “observed” mass-to-flux ratio shows a poor correlation with both the true mass-to-flux ratio and the density structure of the cloud.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".