Investigation of laser ion acceleration in low-density targets using exploded foils
Bibliographic record
Abstract
Intense research is being conducted into sources of laser-accelerated ions and their application. Particular attention is now given to the low-density regime of laser ion acceleration. In this regime, volume effects are expected to dominate, while for solid foils, ion acceleration is directly related to the electron surface density and the number of accelerated ions is limited. Simulations therefore show that it is possible to reach high ion energies with a high number of accelerated ions and a high conversion efficiency. This scheme also leads to less debris than solid foils and is more adapted to high repetition lasers. Due to the difficulty in generating short and dense gas jets experimentally, we have decided to study this regime using very thin foils exploded by a longer, lower intensity pulse. As this regime scales well with laser energy, experiments were recently performed with a high laser energy (∼180 J) on the LLNL Titan laser. A secondary long pulse laser was used to control the density profile of the target. Preliminary analysis suggests that, in this high-energy regime, protons of high energies and with good beam quality were obtained when exploding the foil. We present new simulation results exploring the laser ion acceleration mechanism in laser and plasma conditions close to those of these experiments. These results demonstrate that low-density targets are a promising candidate for an efficient proton source. This source can be optimized by choosing appropriate plasma conditions.
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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 | 0.001 | 0.000 |
| Open science | 0.001 | 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".