Simulating diverse instabilities of dust in magnetized gas
Bibliographic record
Abstract
ABSTRACT Recently, Squire & Hopkins showed that charged dust grains moving through magnetized gas under the influence of a uniform external force (such as radiation pressure or gravity) are subject to a spectrum of instabilities. Qualitatively distinct instability families are associated with different Alfvén or magnetosonic waves and drift or gyro motion. We present a suite of simulations exploring these instabilities, for grains in a homogeneous medium subject to an external acceleration. We vary parameters such as the ratio of Lorentz-to-drag forces on dust, plasma β, size scale, and acceleration. All regimes studied drive turbulent motions and dust-to-gas fluctuations in the saturated state, rapidly amplify magnetic fields into equipartition with velocity fluctuations, and produce instabilities that persist indefinitely (despite random grain motions). Different parameters produce diverse morphologies and qualitatively different features in dust, but the saturated gas state can be broadly characterized as anisotropic magnetosonic or Alfvénic turbulence. Quasi-linear theory can qualitatively predict the gas turbulent properties. Turbulence grows from small to large scales, and larger scale modes usually drive more vigorous gas turbulence, but dust velocity and density fluctuations are more complicated. In many regimes, dust forms structures (clumps, filaments, sheets) that reach extreme overdensities (up to ≫109 times mean), and exhibit substantial substructure even in nearly incompressible gas. These can be even more prominent at lower dust-to-gas ratios. In other regimes, dust self-excites scattering via magnetic fluctuations that isotropize and amplify dust velocities, producing fast, diffusive dust motions.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".