Validation of heat transport modeling using directly driven beryllium spheres
Bibliographic record
Abstract
Recent experiments involving directly driven beryllium spheres are reported. Plasma conditions are measured using Thomson scattering with the probe beam pointed 200, 300, and 400 μm from the surface of the sphere. Laser coupling is assessed using calorimeters that collect scattered light placed at various locations within the target chamber. Laser intensities of 1014 W/cm2 and 2.5×1014 W/cm2 are chosen to minimize unmodeled laser-plasma interactions (LPIs) that lead to laser-target decoupling. Two-dimensional simulations are compared to the interpreted data using the radiation-hydrodynamics code Lasnex. Heat transport is simulated using flux-limited Spitzer–Harm with both high (f = 0.15) and low (f = 0.03) flux limiters and the Schurtz–Nicolai-Busquet (SNB) model. At 1014 W/cm2, all three heat transport models agree well with the measurement, demonstrating that the heat flux is local at low intensities near the measurement locations. At 2.5×1014 W/cm2, the SNB and high flux model roughly match the plasma conditions but predict 2% uncoupled light compared to 10% measured. The use of drive multipliers to match the measured coupled light does not alter the agreement between measured and simulated plasma conditions, suggesting that decoupling due to LPI is unlikely to alter this agreement. The low flux model cannot match the plasma conditions and results in 19% scattered light. The use of a resonant absorption model can be used to bring the simulated scattered light into agreement, but the simulated plasma conditions are still in disagreement with the measurement. For this reason, the low flux model is rejected.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".