Electron kinetic simulations of solid density Al plasmas produced by intense subpicosecond laser pulses. I. Ionization dynamics in 30 femtosecond pulses
Bibliographic record
Abstract
The interaction of a 1018 W/cm2, 30 fs laser pulse with solid Al was simulated with the electron kinetic code “FPI” [J. P. Matte et al., Phys. Rev. Lett. 72, 1208 (1994)] in which an improved average ion module was fully coupled to the electron kinetics. It includes electron impact ionization and excitation and their inverse processes: collisional recombination and de-excitation; as well as radiative decay and pressure ionization. We compare to runs without the inverse processes, and also without atomic physics (with 〈Z〉 set to 11). Atomic physics strongly affects the energy balance and the shape of the distribution function. Line radiation is mostly due to three body recombination into excited states after the peak of the pulse, as the plasma cools down. Despite the atomic processes and the high density, strongly non-Maxwellian distribution functions were obtained due to very steep temperature gradients and strong collisional heating, at the peak of the pulse. However, after the pulse, there is a very rapid thermalization of the electron distribution to which inverse processes strongly contribute.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".