Magnetized filament models for diverging plasma lenses
Bibliographic record
Abstract
ABSTRACT Spherical plasma lens models are known to suffer from a severe overpressure problem, with some observations requiring lenses with central pressures up to millions of times in excess of the ambient interstellar medium. There are two ways that lens models can solve the overpressure problem: a confinement mechanism exists to counter the internal pressure of the lens, or the lens has a unique geometry, such that the projected column-density appears large to an observer. This occurs with highly asymmetric models, such as edge-on sheets or filaments, with potentially low volume–density. In the first part of this work we investigate the ability of non-magnetized plasma filaments to mimic the magnification of sources seen behind spherical lenses and we extend a theorem from gravitational lens studies regarding this model degeneracy. We find that for plasma lenses, the theorem produces unphysical charge density distributions. In the second part of the work, we consider the plasma lens overpressure problem. Using magnetohydrodynamics, we develop a non self-gravitating model filament confined by a helical magnetic field. We use toy models in the force-free limit to illustrate novel lensing properties. Generally, magnetized filaments may act as lenses in any orientation with respect to the observer, with the most high-density events produced from filaments with axes near the line of sight. We focus on filaments that are perpendicular to the line of sight that show the toroidal magnetic field component may be observed via the lens rotation measure.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".