Experimental and numerical investigation of the impact of helical coil targets on laser-driven proton and carbon accelerations
Bibliographic record
Abstract
Laser-driven ion acceleration, as produced by interaction of a high-intensity laser with a target, is a growing field of interest. One of the current challenges is to enhance the acceleration process, i.e., to increase the produced ion energy and the ion number and to shape the energy distribution for future applications. In this paper, we investigate the effect of helical coil (HC) targets on the laser–matter interaction process using a 150 TW laser. We demonstrate that HC targets significantly enhance proton acceleration, improving energy bunching and beam focusing and increasing the cutoff energy. For the first time, we extend this analysis to carbon ions, revealing a marked reduction in the number of low-energy carbon ions and the potential for energy bunching and post-acceleration through an optimized HC design. Simulations using the particle-in-cell code SOPHIE confirm the experimental results, providing insights into the current propagation and ion synchronization mechanisms in HCs. Our findings suggest that HC targets can be optimized for multispecies ion acceleration.
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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 | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".