Saturation of multi-laser beams laser-plasma instabilities from stochastic ion heating
Bibliographic record
Abstract
Cross-beam energy transfer (CBET) has been used as a tool on the National Ignition Facility (NIF) since the first energetics experiments in 2009 to control the energy deposition in ignition hohlraums and tune the implosion symmetry. As large amounts of power are transferred between laser beams at the entrance holes of NIF hohlraums, the presence of many overlapping beat waves can lead to stochastic ion heating in the regions where laser beams overlap [P. Michel et al., Phys. Rev. Lett. 109, 195004 (2012)]. This increases the ion acoustic velocity and modifies the ion acoustic waves’ dispersion relation, thus reducing the plasma response to the beat waves and the efficiency of CBET. This pushes the plasma oscillations driven by CBET in a regime where the phase velocities are much smaller than both the electron and ion thermal velocities. CBET gains are derived for this new regime and generalized to the case of multi ion species plasmas.
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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 | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 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".