Relativistic turning acceleration of radiation belt electrons by whistler mode chorus
Bibliographic record
Abstract
We perform test particle simulations assuming whistler mode chorus wave packets that are generated at the geomagnetic equator propagate away from the equator in both poleward directions. While electrons in the energy range 10–100 keV are primarily responsible for the generation of chorus waves through pitch angle diffusion into the loss cone, it has been found that a fraction of the higher‐energy electrons from a few hundred keV to a few MeV are effectively accelerated by chorus due to a special nonlinear trapping process called relativistic turning acceleration (RTA). This mechanism has been recently demonstrated for a coherent whistler mode wave packet with a constant amplitude and constant frequency. In the present study we confirm that the RTA process takes place for a wave packet with variable frequency such as that occurring in a rising tone of chorus emissions. We study the efficiency of the RTA process for different particle energies. A Green's function method is used to describe the evolution of the particle energy distribution function. The RTA process due to chorus emissions creates a high‐energy tail in the electron energy distribution function. The shape of the high‐energy tail is determined by the distribution function of the seed electrons in the lower‐energy range. RTA can accelerate electrons in a much shorter timescale than that estimated by quasi‐linear diffusion theory, e.g., it typically takes tens of minutes to hours for ∼100 keV seed electrons to be accelerated to energies of a few MeV by RTA.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".