Heuristic Description of Perpendicular Particle Transport in Turbulence with Super-diffusive Magnetic Field Lines
Bibliographic record
Abstract
Abstract Recently a heuristic description of collisionless perpendicular diffusion of energetic particles was presented. The latter approach describes the transport of energetic particles across a mean magnetic field based on simple physical arguments. Although this approach was developed with the intention to improve our understanding of perpendicular diffusion, this heuristic approach also provided some interesting quantitative results such as an explanation of the factor a 2 used in the past to balance out inaccuracies of systematic analytical theories. However, the aforementioned heuristic approach is based on the assumption that magnetic field lines become diffusive after overcoming the initial free-streaming regime. In the current paper we alter the heuristic approach to make it applicable for turbulence spectra leading to super-diffusive magnetic fields lines. It is argued that particle diffusion is still restored in the late time limit. In the high-energy limit this recovery of diffusion is based on a hybrid model in which particles move half ballistically and half diffusively in the parallel direction. Furthermore, this leads to the relation between perpendicular and parallel mean free paths of the energetic particles. This type of transport was obtained in the past from test-particle simulations as well as systematic analytical theories. In the current paper we present the first time an explanation of this behavior based on simple physical arguments.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.003 | 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".