Effects of overshoots on electron distributions upstream and downstream of quasi‐perpendicular collisionless shocks
Bibliographic record
Abstract
We use test particle simulations to investigate the effect of overshoots on electron distributions upstream and downstream of quasi‐perpendicular collisionless shocks as functions of angle θbn between upstream magnetic field and shock normal. The main results are: (1) The development of overshoots leads to obvious loss cone structures in upstream electron distributions, increases the reflection of upstream electrons, and reduces the leakage of downstream electrons. An electron beam associated with the escape cutoff effect exists in upstream reduced electron distribution functions for large θbn shocks. (2) Overshoots significantly affect the downstream electron distribution, too, preventing the upstream and downstream electrons with high perpendicular speeds from crossing the shock and leading to a loss cone and beam features rather than arc features in downstream distributions. (3) The shock‐reflected electrons conserve their magnetic moments on average. Demagnetized electrons are mainly those transmitted, and most are overadiabatic electrons. The assumption of conservation of magnetic moment is an increasingly good approximation as θbn increases. (4) The major temporal changes in ramp overshoot structure expected from shock nonstationarity should lead to significant changes in electron distributions and associated waves upstream and downstream.
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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 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".