Laser acceleration of low emittance, high energy ions and applications
Bibliographic record
Abstract
Laser-accelerated ion sources have exceptional properties, i.e. high brightness and high spectral cut-off (56 MeV at present), high directionality and laminarity (at least 100-fold better than conventional accelerators beams), short burst duration (ps). Thanks to these properties, these sources open new opportunities for applications. Among these, we have already explored their use for proton radiography of fields in plasmas and for warm dense matter generation. These sources could also stimulate development of compact ion accelerators or be used for medical applications. To extend the range of applications, ion energy and conversion efficiency must however be increased. Two strategies for doing so using present-day lasers have been successfully explored in LULI experiments. In view of applications, it is also essential to control (i.e. collimate and energy select) these beams. For this purpose, we have developed an ultra-fast laser-triggered micro-lens providing tuneable control of the beam divergence as well as energy selection.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".