Generation of intense magnetic fields using orbital angular momentum modes of light in plasmas
Bibliographic record
Abstract
Summary form only given. Circularly polarized light has been successfully used to generate high strength axial magnetic fields in plasmas using high intensity laser pulses. These strong magnetic fields have been able to reach magnitudes approaching 1000 Tesla [1]. High strength magnetic fields have potential uses for guiding electron beams onto targets, such as for fast ignition of laser compressed fusion targets. Recent interest in orbital angular momentum (OAM) modes has shown that even linearly polarized light can potentially give rise to axial magnetic fields [2,3]. Laguerre-Gaussian (LG) modes carry both spin and OAM, generated by the rotating wave vector as opposed to that of a plane wave which contains only a spin component. In this work, the mathematics, geometry and simulation of the modes are discussed together with approaches to generating these OAM modes without the use of a spatial light modulator (SLM). Currently an experiment is being prepared to create such OAM modes using phase plates, while measurements of the magnetic field strengths produced will be measured using Zeeman splitting of bound-bound transitions of ion species in the plasma. Imaging the fields using inverse Faraday rotation measurements will also be investigated. The generation of such fields and the initial proposed measurements will be discussed.
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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 | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".