Electron acceleration due to inertial Alfvén waves in a non‐Maxwellian plasma
Bibliographic record
Abstract
We investigate parallel electron acceleration due to inertial Alfvén wave pulses in the presence of Lorentzian (kappa) distribution functions which possess high‐energy tails. A linear kinetic dispersion relation for inertial Alfvén waves is derived whose solutions are used to guide the analysis of the simulation results. The dispersion relation solutions show that the parallel phase velocity of linear waves is unchanged when Lorentzian distribution functions are considered instead of Maxwellian distribution functions. The solutions also indicate that Landau damping is increased for low values of spectral index, κ, implying that wave‐particle interactions are enhanced for Lorentzian distribution functions. We test this hypothesis by performing self‐consistent kinetic simulations and show that the energy content of resonant beam electrons significantly increases with decreasing κ. The dependence of this process on pulse amplitude and perpendicular scale length is investigated, and it is shown that for the same pulse parameters, resonant electron beams are generated more efficiently in a Lorentzian plasma compared to a Maxwellian plasma. The energy range of resonant beam electrons are also presented, and it is noted that for low values of κ it is possible to generate electrons with energy of a few keV, even for relatively small‐amplitude pulses with peak perpendicular electric fields of the order of 20 mV/m. We also show that the percentage of wave Poynting flux which is converted into electron energy flux depends upon the value of κ, the perpendicular scale length, and the initial amplitude of the inertial Alfvén wave.
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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 | 0.000 | 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) | 0.001 | 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".