Parallel whistler instability in a plasma with an anisotropic bi‐kappa distribution
Bibliographic record
Abstract
The parallel‐propagating whistler instability in a magnetized plasma of electrons and positive ions having bi‐kappa velocity distributions is investigated for a wide range of parameters. The threshold condition for instability does not depend on the index, κe, of the electron bi‐kappa distribution, but the maximum growth rate depends strongly on this parameter. The functional dependence of the growth rate on κe is strongly influenced by the value of the electron temperature anisotropy. For small anisotropies the maximum growth rate is enhanced by the presence of a low‐κe power‐law tail, as is well known. At larger temperature anisotropies, however, the reverse applies. A hard tail on the electron velocity distribution is deleterious to the whistler instability in this parameter region. For electron temperature anisotropies intermediate between those described, the (maximum with respect to wave number) growth rate maximizes for a particular κe value. The dependence of the maximum growth rate on κe in this electron temperature anisotropy regime is non‐monotonic. For a fixed value of the electron temperature anisotropy, the growth rate is strongly controlled by the parallel electron beta. Larger values of this parameter increase the growth rate, and conversely. The parallel electron beta also governs the value of the electron temperature anisotropy that separates the different types of monotonic behavior of the growth rate described above. The instability is not very sensitive to the electron temperature, provided cases with similar parallel electron beta values are compared. It is pointed out that all whistler dispersion relations, regardless of the value of κe used, pass through a common (ω, k) point. This point is closely related to the instability threshold condition. A novel application is suggested.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 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".