Shock Drift Acceleration in presence of turbulence: A simple model
Bibliographic record
Abstract
Analytic treatments of particle acceleration by collisionless shocks have commonly been based on the assumption that the shock surface is quasi-planar with length scales larger than the particle gyroradius. Within this framework, the derived particle distributions are not in full agreement with the observations. Recent theoretical studies as well as numerical simulations indicate that ion scales shock fluctuations could account for several features observed in shock-associated energetic particle velocity distributions. We have developed a simple model based on the shock drift acceleration mechanism in which both the shock normal direction and the shock mirror ratio N = B2=B1 are subject to random fluctuations. While, the maximum particle energy gain is dependent upon N, the pitch angle distribution is dependent to both N and shock geometry. The fluctuations could be induced by shock turbulence inherent to the shock nonstationarity occuring for high Mach number and for a wide range of shock geometries. For low energy particles, we derive the probability distributions functions f(vjj) and f(v?) of ions escaping upstream; we show that the distributions significantly deviate from the conventional Maxwellian signature. At higher energy, the derived energy spectrum extends to higher energy in comparison to the one obtained when the fluctuations are suppressed. The derived results are compared to ion distributions and spectra observed upstream of the Earth bow shock. They also in turn allow us to draw possible analogy with neutral fluid turbulence involving higher moments of the particle distribution.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".