Formation of double layers and evolution of the distribution functions during ion acceleration driven by a high-intensity short laser pulse normally incident on thin foils
Bibliographic record
Abstract
Abstract We use an Eulerian Vlasov code, which solves the one-dimensional relativistic Vlasov–Maxwell equations for both electrons and ions, to follow in details the evolution of the distribution functions and the mechanism of the formation and evolution of double layers during ion acceleration driven by a high-intensity circularly polarized short laser pulse (12 ω−1 where ω is the laser angular frequency) normally incident on a thin dense foil. We compare three cases with a high-density deuterium plasma target of total thickness 1.767 cω−1 and constant n/ncr = 100, where ncr is the critical density, and where the laser intensity is varied from a situation where the target is opaque to the laser pulse (normalized vector potential or quiver momentum a0 = 80), to a situation where, above a critical laser intensity, a very small fraction of the laser pulse is transmitted through the target (a0 = 90), and finally to a situation where a more important fraction is transmitted through the target (a0 = 100). The dynamics of ion and electron acceleration are quite different in the three cases, and are followed in detail by the Eulerian Vlasov code, which allows an accurate representation of the distribution function. In the intermediate case, the Vlasov code has revealed a remarkably well-developed spiral structure in the phase space of the electron distribution function, which is associated with large sawtooth modulations in the electron density profiles.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".