Damping of Magnetohydrodynamic Turbulence in Solar Flares
Bibliographic record
Abstract
We describe the cascade of plasma waves or turbulence injected, presumably by reconnection, at scales comparable to the size of a solar flare loop, L ~ 10 9 cm, to scales comparable to elementary particle gyroradii and evaluate their damping at small scales by various mechanisms. We show that the classical viscous damping valid on scales larger than the collision mean free path (~10 8 cm) is unimportant for magnetically dominated or low-β plasmas and the primary damping mechanism is the collisionless damping by the background particles. We show that the damping rate is proportional to the total random momentum density of the particles. For solar flare conditions this means that in most flares, except the very large ones in which essentially all background electrons are accelerated into a nonthermal distribution, the damping is dominated by thermal background electrons. In general, damping by protons is negligible compared to that of electrons except for rare proton-dominated flares with strong nuclear gamma-ray line emission and for quasi-perpendicular propagating waves. We also determine the critical scale below which the damping becomes important and the spectrum of the turbulence steepens. We show that this scale has a strong dependence on the propagation angle of the waves with respect to the background magnetic field, resulting in a highly anisotropic spectral distribution, with quasi-parallel and quasi-perpendicular waves cascading undamped to small scales.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".