Parallel Collisionless Shocks in Strongly Magnetized Electron–Ion Plasma. I. Temperature Anisotropies
Bibliographic record
Abstract
Abstract Collisionless electron–ion shocks are fundamental to astrophysical plasmas, yet their behavior in strong magnetic fields remains poorly understood. Using particle-in-cell simulations with the SHARP-1D3V code, we investigate the role of the ion magnetization parameter σ i in parallel shock transitions. Strongly magnetized converging flows ( σ i > 1) exhibit lower-density compression ratios ( R ∼ 2), smaller entropy jumps, and suppressed particle acceleration while maintaining pressure anisotropy stability due to conserved perpendicular temperatures across the transition region, alongside increased parallel temperatures. In contrast, weakly magnetized shocks drive downstream mirror and firehose instabilities due to ion temperature anisotropy, which are suppressed in strongly magnetized cases. Additionally, weakly magnetized shocks exhibit the onset of a suprathermal population induced by shock-drift acceleration, with most of the upstream kinetic energy thermalized for both electrons and ions in the downstream region. Our results demonstrate that perpendicular temperatures for both species are conserved in weakly and strongly magnetized cases and highlight deviations from standard ideal magnetohydrodynamic behavior in strongly magnetized cases. These findings provide critical insights into the role of magnetic fields in parallel collisionless astrophysical shocks.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".