Design and optimization of a laser-PIXE beamline for material science applications
Bibliographic record
Abstract
Abstract Multi-MeV proton beams can be generated by irradiating thin solid foils with ultra-intense (>1018 W/cm2) short laser pulses. Several of their characteristics, such as high bunch charge and short pulse duration, make them a complementary alternative to conventional radio frequency-based accelerators. A potential material science application is the chemical analysis of cultural heritage (CH) artifacts. The complete chemistry of the bulk material (ceramics, metals) can be retrieved through sophisticated nuclear techniques such as particle-induced X-ray emission (PIXE). Recently, the use of laser-generated proton beams was introduced as diagnostics in material science (laser-PIXE or laser-driven PIXE): Coupling laser-generated proton sources to conventional beam steering devices successfully enhances the capture and transport of the laser-accelerated beam. This leads to a reduction of the high divergence and broad energy spread at the source. The design of our hybrid beamline is composed of an energy selector, followed by permanent quadrupole magnets aiming for better control and manipulation of the final proton beam parameters. This allows tailoring both, mean proton energy and spot sizes, yet keeping the system compact. We performed a theoretical study optimizing a beamline for laser-PIXE applications. Our design enables monochromatizing the beam and shaping its final spot size. We obtain spot sizes ranging between a fraction of mm up to cm scale at a fraction of nC proton charge per shot. These results pave the way for a versatile and tunable laser-PIXE at a multi-Hz repetition rate using modern commercially available laser systems.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".