Inverse faraday effect magnetic field generation in laser induced plasma
Bibliographic record
Abstract
Summary form only given. Laser plasma interactions with high intensity laser pulses can produce high magnetic fields in the 10's of MG range. One technique for generating high magnetic fields in under dense plasmas is via the Inverse Faraday Effect (IFE) which has been shown to induce axial magnetic fields in the MG range using circularly polarized light [1]. It is also proposed that similar fields could be induced using linearly polarized light where higher order angular momentum modes [2] are employed to couple the required angular momentum to the electrons. We wish to study this magnetic field generation in under dense plasma and are carrying out a simulation study of the expected fields.IFE is a phenomenon in which the circularly polarized light propagates through a non-linear medium, transmits angular momentum to the electrons and induces an axial magnetic field [3]. This picture gets more complicated with hot electron generation by the propagation of the intense laser pulse through the plasma, which in addition generates an axial current and solenoidal magnetic field. We have carried out initial Large Scale PIC (LSP) simulations to predict the scaling law for this hot electron generation and hence the expected magnetic field levels. Data will be shown for various parameters of laser plasma interaction with different densities and intensities in the threshold relativistic intensity range of 1017 to 1019 Wcm-2.
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 | 0.000 |
| 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 | 0.000 |
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".