Development of a Platform at the Matter in Extreme Conditions End Station for Characterization of Matter Heated by Intense Laser-Accelerated Protons
Bibliographic record
Abstract
High-intensity short-pulse lasers have made possible the generation of energetic proton beams, unlocking numerous applications in high energy density science. One such application is uniform and isochoric heating of materials to the warm dense matter (WDM) state. We have developed a new experimental platform to simultaneously create and probe WDM at the matter in extreme conditions (MEC) end station at the Linac Coherent Light Source (LCLS). The short pulse optical laser (delivering up to 1 J in 45 fs) and the ultrabright LCLS X-ray laser with tunable frequency, respectively, deliver high power required to heat materials to WDM and precision-timed high-resolution X-rays to probe them. The laser-accelerated proton beam driven from a flat 1.5-μm Cu foil was first measured then directed to a secondary sample of Al or polypropylene (PP), typically 300-400 μm away. The time evolution of the sample electron temperature was measured using streaked optical pyrometry, where we observed a peak temperature of 0.9 ± 0.15 eV on the rear surface of an Al sample heated by the proton beam. Simulations using the hybrid-PIC code LSP and the rad-hydro code HELIOS show that a measured proton beam can heat Al to approximately 4 eV and PP to 1 eV if instead focused by a hemispherical Cu target. Through additional LSP simulations, we anticipate creating hotter WDM states (~20 eV) by increasing the laser energy to 10 J and keeping the other laser parameters fixed.
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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 | 0.000 |
| 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 | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".