Crossed beam energy transfer in the presence of laser speckle ponderomotive self-focusing and nonlinear sound waves
Bibliographic record
Abstract
Crossed beam energy transfer, CBET, is investigated by taking into account the speckle structure of smoothed laser beams that overlap in a plasma with an inhomogeneous flow profile. Using the two-dimensional simulation code Harmony, it is shown how ponderomotive self-focusing of laser speckles in crossing beams can significantly affect the transfer of energy from one beam to the other. The role of plasma flow in speckle self-focusing is investigated and revisited, in particular its consequences in terms of redirection and increasing angular spread of the laser beams due to beam bending and plasma-induced smoothing, respectively. In close-to-sonic flow, the onset of self-focusing in the beam speckle structure occurs at considerably lower beam intensities than expected for the case without flow. CBET and speckle self-focusing can, hence, occur together when two crossed beams with equal frequency resonantly exchange energy via their ponderomotively driven density perturbations flowing with sound speed. From the simulations, it is found that consequences of ponderomotive self-focusing can be expected above an average intensity threshold scaling as IL∼2×1014 W cm−2(λ0/1 μm)−2(Te/ keV ), with an impact on the spatial and temporal coherence of the transmitted light. The density perturbations due to the ponderomotive force of the crossing beams can locally be enhanced in self-focusing speckles, partly leading to shock-like structures. These structures eventually increase the effect of plasma-induced smoothing and are at the origin of the stronger angular spread.
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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 | 0.001 |
| 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) | 0.002 | 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".