Propagation of Electron-Acoustic Waves in a Plasma with Suprathermal Electrons
Bibliographic record
Abstract
Electron-acoustic waves occur in space and laboratory plasmas where two distinct electron populations exist, namely cool and hot electrons. The observations revealed that the hot electron distribution often has a long-tailed suprathermal (non-Maxwellian) form. The aim of the present study is to investigate how various plasma parameters modify the electron-acoustic structures. We have studied the electron-acoustic waves in a collisionless and unmagnetized plasma consisting of cool inertial electrons, hot suprathermal electrons, and mobile ions. First, we started with a cold one-fluid model, and we extended it to a warm model, including the electron thermal pressure. Finally, the ion inertia was included in a two-fluid model. The linear dispersion relations for electron-acoustic waves depicted a strong dependence of the charge screening mechanism on excess suprathermality. A nonlinear (Sagdeev) pseudopotential technique was employed to investigate the existence of electron-acoustic solitary waves, and to determine how their characteristics depend on various plasma parameters. The results indicate that the thermal pressure deeply affects the electron-acoustic solitary waves. Only negative polarity waves were found to exist in the one-fluid model, which become narrower as deviation from the Maxwellian increases, while the wave amplitude at fixed soliton speed increases. However, for a constant value of the true Mach number, the amplitude decreases for increasing suprathermality. It is also found that the ion inertia has a trivial role in the supersonic domain, but it is important to support positive polarity waves in the subsonic domain.
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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 | 0.000 |
| 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 | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".