Fully microscopic analysis of laser-driven finite plasmas using the example of clusters
Bibliographic record
Abstract
We discuss a microscopic particle-in-cell (MicPIC) approach that allows bridging of the microscopic and macroscopic realms of laser-driven plasma physics. The simultaneous resolution of collisions and electromagnetic field propagation in MicPIC enables the investigation of processes that have been inaccessible to rigorous numerical scrutiny so far. This is illustrated by the two main findings of our analysis of pre-ionized, resonantly laser-driven clusters, which can be realized experimentally in pump–probe experiments. In the linear response regime, MicPIC data are used to extract the individual microscopic contributions to the dielectric cluster response function, such as surface and bulk collision frequencies. We demonstrate that the competition between surface collisions and radiation damping is responsible for the maximum in the size-dependent lifetime of the Mie surface plasmon. The capacity to determine the microscopic underpinning of optical material parameters opens new avenues for modeling nano-plasmonics and nano-photonics systems. In the non-perturbative regime, we analyze the formation and evolution of recollision-induced plasma waves in laser-driven clusters. The resulting dynamics of the electron density and local field hot spots opens a new research direction for the field of attosecond science.
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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 | 0.001 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".