Particle-in-cell simulations – ion beam instabilities and the generation of Alfvén and whistler waves in low β plasma
Bibliographic record
Abstract
ABSTRACT Ion beam-driven instabilities in a collisionless space plasma with low β, i.e. low plasma and magnetic pressure ratio, are investigated using particle-in-cell (PIC) simulations. Specifically, the effects of different ion drift velocities on the development of Buneman and resonant electromagnetic (EM) right-handed (RH) ion beam instabilities are studied. Our simulations reveal that both instabilities can be driven when the ion beam drift exceeds the theoretical thresholds. The Buneman instability, which is weakly triggered initially, dissipates only a small fraction of the kinetic energy of the ion beam while causing significant electron heating, owing to the small electron-ion mass ratio. However, we find that the ion beam-driven Buneman instability is quenched effectively by the resonant EM RH ion beam instability. Instead, the resonant EM RH ion beam instability dominates when the ion drift velocity is larger than the Alfvén speed, leading to the generation of RH Alfvén waves and RH whistler waves. We find that the intensity of Alfvén waves decreases with decrease of ion beam drift velocity, while the intensity of whistler waves increases. Our results provide new insights into the complex interplay between ion beams and plasma instabilities in low β collisionless space plasmas.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".