A novel quiescent quasi-steady state of a toroidal electron plasma
Bibliographic record
Abstract
The existence of a novel quiescent quasi-steady state of the toroidal electron cloud is reported. This is achieved by first constructing a maximum entropy mean-field solution for pure electron plasma at zero-inertia limit (ρ¯L/L→0, where ρ¯L is average electron Larmor radius and L is typical mean spatial gradient length scale), which is then used as “seed” solution to a high fidelity 3D3V PIC solver, at finite density of pure electron plasma in small aspect ratio toroidal configuration. The electron cloud is shown to attain a quiescent quasi-steady state satisfying full equations of motion and hence accurate to all orders in ρ¯L/L, with far superior confinement properties as compared to typical initial condition used in today's laboratories. Salient features include the absence of center of charge motion, naturally shaped centrally peaked density, and potential concentric surfaces. The variation of temperatures T¯∥(R,t) and T¯⊥(R,t) (averaged over the toroidal direction) with major radius R is reported for the first time for a toroidal electron plasma. For the small aspect ratio of O(1) considered here, the temperature profiles are such that T¯∥(R,t) and T¯⊥(R,t) fall with R as 1/R2 and 1/R3, respectively. Our solution to this long-standing problem of finding a quiescent quasi-steady of a toroidal charge cloud may have direct relevance to not only pure electron plasma but also to pure ion plasma.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".