20-dimensional surrogate-assisted Bayesian optimization of laser-driven proton beams
Bibliographic record
Abstract
Laser-driven proton acceleration, as obtained by the interaction of a high-intensity laser with matter, is a promising technique for generating high-quality proton beams. One of the main challenges is increasing the maximum proton energy. Here, we demonstrate a 70% increase in the maximum energy of laser-driven protons by optimizing the wavefront of the intense laser using machine learning: This was accomplished through adaptive control of a deformable mirror (DM) using a multi-step Random Forest surrogate-assisted Bayesian optimization approach. Starting from zeroed DM actuator voltages, our method identified an optimal configuration using 20 out of 48 actuators, requiring fewer than 150 experimental data samples. Our method surpassed conventional wavefront correction by 24%, which typically minimizes aberrations to converge toward a flat wavefront by leveraging real-time feedback from a wavefront sensor. This data-driven method integrating advanced wavefront control challenges the preference for correcting aberrations to achieve a flatter wavefront in laser-driven ion acceleration. We also propose a strategy for optimizing short focal length ion accelerators at facilities where measuring the wavefront at nominal full laser power is not implemented.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".