Hot-electron generation at direct-drive ignition-relevant plasma conditions at the National Ignition Facility
Bibliographic record
Abstract
Laser–plasma interaction instabilities can be detrimental for direct-drive inertial confinement fusion by generating high-energy electrons that preheat the target. An experimental platform has been developed and fielded on the National Ignition Facility to investigate hot-electron production from laser–plasma instabilities at direct-drive ignition-relevant conditions. The radiation-hydrodynamic code DRACO has been used to design planar-target experiments that generate plasma and interaction conditions comparable to direct-drive ignition designs: IL ∼ 1015 W/cm2, Te > 3 keV, and density-gradient scale lengths of Ln ∼ 600 μm in the quarter-critical density region. The hot-electron properties were inferred by comparing the experimentally observed hard x-ray spectra to Monte Carlo simulations of hard x-ray emission from hot electrons depositing energy in the target. Hot-electron temperatures of ∼40 keV to 60 keV and the fraction of laser energy converted to hot electrons of ∼0.5% to 5% were inferred in plastic targets for laser intensities at the quarter-critical density surface of (∼4 to 14) × 1014 W/cm2. The use of silicon ablators was found to mitigate the hot-electron preheat by increasing the threshold laser intensity for hot-electron generation from ∼3.5 × 1014 W/cm2 in plastic to ∼6 × 1014 W/cm2 in silicon. The overall hot-electron production is also reduced in silicon ablators when the intensity threshold is exceeded.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".