Sources and space–time distribution of the electromagnetic pulses in experiments on inertial confinement fusion and laser–plasma acceleration
Bibliographic record
Abstract
When high-energy and high-power lasers interact with matter, a significant part of the incoming laser energy is transformed into transient electromagnetic pulses (EMPs) in the range of radiofrequencies and microwaves. These fields can reach high intensities and can potentially represent a significative danger for the electronic devices placed near the interaction point. Thus, the comprehension of the origin of these electromagnetic fields and of their distribution is of primary importance for the safe operation of high-power and high-energy laser facilities, but also for the possible use of these high fields in several promising applications. A recognized main source of EMPs is the target positive charging caused by the fast-electron emission due to laser-plasma interactions. The fast charging induces high neutralization currents from the conductive walls of the vacuum chamber through the target holder. However, other mechanisms related to the laser-target interaction are also capable of generating intense electromagnetic fields. Several possible sources of EMPs are discussed here and compared for high-energy and high-intensity laser-matter interactions, typical for inertial confinement fusion and laser-plasma acceleration. The possible effects on the electromagnetic field distribution within the experimental chamber, due to particle beams and plasma emitted from the target, are also described. This article is part of a discussion meeting issue 'Prospects for high gain inertial fusion energy (part 2)'.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".