Design and optimization of a compact laser-driven proton beamline
Bibliographic record
Abstract
Abstract Laser-accelerated protons, generated by irradiating a solid target with a short, energetic laser pulse at high intensity (I > 1018 W·cm−2), represent a complementary if not outperforming source compared to conventional accelerators, due to their intrinsic features, such as high beam charge and short bunch duration. However, the broadband energy spectrum of these proton sources is a bottleneck that precludes their use in applications requiring a more reduced energy spread. Consequently, in recent times strong effort has been put to overcome these limits and to develop laser-driven proton beamlines with low energy spread. In this paper, we report on beam dynamics simulations aiming at optimizing a laser-driven beamline - i.e. a laser-based proton source coupled to conventional magnetic beam manipulation devices - producing protons with a reduced energy spread, usable for applications. The energy range of investigation goes from 2 to 20 MeV, i.e. the typical proton energies that can be routinely obtained using commercial TW-power class laser systems. Our beamline design is capable of reducing the energy spread below 20%, still keeping the overall transmission efficiency around 1% and producing a proton spot-size in the range of 10 mm2. We briefly discuss the results in the context of applications in the domain of Cultural Heritage.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".