Fast Dissipation of Colliding Alfvén Waves in a Magnetically Dominated Plasma
Bibliographic record
Abstract
Abstract Magnetic energy around compact objects often dominates over plasma rest mass, and its dissipation can power the object’s luminosity. We describe a dissipation mechanism that works faster than magnetic reconnection. The mechanism involves two strong Alfvén waves with anti-aligned magnetic fields B 1 and B 2 that propagate in opposite directions along the background magnetic field B 0 and collide. The collision forms a thin current sheet perpendicular to B 0, which absorbs the incoming waves. The current sheet is sustained by an electric field E breaking the magnetohydrodynamic condition E < B and accelerating particles to high energies. We demonstrate this mechanism with kinetic plasma simulations using a simple setup of two symmetric plane waves with amplitude A = B 1/B 0 = B 2/B 0 propagating in a uniform B 0. The mechanism is activated when A > 1/2. It dissipates a large fraction of the wave energy, f = (2A − 1)/A 2, reaching 100% when A = 1. The plane geometry allows one to see the dissipation process in a one-dimensional simulation. We also perform two-dimensional simulations, enabling spontaneous breaking of the plane symmetry by the tearing instability of the current sheet. At moderate A of main interest, the tearing instability is suppressed. Dissipation transitions to normal, slower, magnetic reconnection at A ≫ 1. The fast dissipation described in this paper may occur in various objects with perturbed magnetic fields, including magnetars, jets from accreting black holes, and pulsar wind nebulae.
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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 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.001 | 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".