Effects of Superthermal Plasmas on the Linear Growth of Multiband EMIC Waves
Bibliographic record
Abstract
Abstract Observations show that particle velocity distributions in space plasmas generally exhibit a non-Maxwellian high-energy tail that can be well fitted with kappa distributions. To better understand the correlation between realistic particle velocity distributions and plasma wave excitation, we investigate the linear cyclotron instability of multiband electromagnetic ion cyclotron (EMIC) waves in a kappa plasma containing hot anisotropic protons, which provides the free energy for the wave growth. We find that the effects of superthermal plasmas on EMIC wave instability have a strong dependence on the emission band, temperature anisotropy A hp, and parallel beta β hp of hot protons. For H+ and He+ band EMIC waves, the maximum growth rates exhibit distinct behaviors with the variation of the spectral index κ of kappa distributions for different A hp values. The maximum growth rates decrease with increasing κ-value for low A hp and increase with increasing κ-value for high A hp. For O+ band waves, the effects of superthermal plasmas on the maximum growth rate are strongly controlled by β hp. For low β hp, the growth rate decreases monotonically with increasing κ-value for all A hp. For high β hp, increase of κ-value tends to enhance the wave growth for intermediate A hp and to suppress the wave growth otherwise. Our results also indicate that the presence of a high-energy tail tends to decrease the real frequency corresponding to the maximum growth rate for all three bands. While the minimum electron resonant energy for O+ band EMIC waves decreases as the κ-value increases, the minimum electron resonant energies for H+ and He+ band waves remain unaffected.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".