Clusters in intense XUV pulses: Effects of cluster size on expansion dynamics and ionization
Bibliographic record
Abstract
We examine the effect of cluster size on the interaction of Ar${}_{55}$-Ar${}_{2057}$ with intense extreme ultraviolet (XUV) pulses, using a model we developed earlier that includes ionization via collisional excitation as an intermediate step. We find that the dynamics of these irradiated clusters is dominated by collisions. Larger clusters are more highly collisional, produce higher charge states, and do so more rapidly than smaller clusters. Higher charge states produced via collisions are found to reduce the overall photon absorption, since charge states of Ar${}^{2+}$ and higher are no longer photoaccessible. We call this mechanism collisionally reduced photoabsorption, and it decreases the effective cluster photoabsorption cross section by more than 30% for Ar${}_{55}$ and 45% for Ar${}_{2057}$. The time evolution of the electron kinetic energy distribution begins as a (mostly) Maxwellian distribution. Further, the electron velocity distribution of large clusters quickly become isotropic while smaller clusters retain the inherent anisotropy created by photoionization. Last, the total electron kinetic-energy distribution is integrated over the spatial profile of the laser and the log-normal distribution of cluster size for comparison with a recent experiment [C. Bostedt et al., Phys. Rev. Lett. 100, 133401 (2008)], and good agreement is found.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".