Electromagnetic Turbulence in the Electron Current Layer to Drive Magnetic Reconnection
Bibliographic record
Abstract
Abstract Magnetic reconnection is a natural energy converter that can have a significant impact on global processes in space, astrophysics, and fusion plasmas. Macroscopic modeling of reconnection is crucial in understanding the global responses to local kinetic processes. The key issue in developing the reconnection model is the description of the magnetic dissipation around the x-line to drive reconnection. In collisionless plasma, the dissipation can be generated by plasma turbulence through wave–particle interactions. However, the mechanisms to yield turbulence and dissipation in the reconnection current layer are currently poorly understood. In this study, we show, using three-dimensional particle-in-cell simulations, that the electron Kelvin–Helmholtz instability plays a primary role in driving intense electromagnetic turbulence leading to the dissipation and electron heating. We find that the ions hardly react to the turbulence, which indicates that the turbulence does not cause significant momentum exchange between electrons and ions resulting in electrical resistivity. It is demonstrated that the dissipation is mainly caused by viscosity associated with electron momentum transport across the current layer. The present results suggest a fundamental modification of the current magnetohydrodynamics models using the resistivity to generate the dissipation.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".