Suppressing cross-beam energy transfer with broadband lasers
Bibliographic record
Abstract
The scattering of laser light due to cross-beam energy transfer (CBET) is an undesirable process in direct-drive inertial confinement fusion (ICF) that degrades both the compression and symmetry of the imploding target. Here, we present results from laser-plasma interaction simulations performed with the wave-based code LPSE that explore two techniques for suppressing CBET in frequency-tripled, Nd:glass laser beams crossing in a transonic plasma: a.) frequency detuning using two or three discrete “colors” of narrowband laser light; and b.) broad laser bandwidth. We find that for beams modeled with random speckle patterns, distributed phase plates and polarization smoothing, and for plasma conditions similar to those on the National Ignition Facility, the former method reduces CBET to an extent, but the degree of mitigation plateaus once the frequency separation greatly exceeds the resonance width of the CBET instability. Broadband lasers, on the other hand, are predicted to suppress CBET completely at a bandwidth of about 8 THz (Δ ω/ω0 ≃ 1%, where Δ ω/2π and ω0 are the laser bandwidth and angular frequency, respectively) for the same conditions. Although the Nd:glass lasers used for ICF research today have bandwidths far below this value, the spectra from such lasers could likely be broadened to multi-terahertz levels by utilizing stimulated rotational Raman scattering in a gaseous diatomic medium. Alternatively, the required bandwidth could be obtained with an excimer laser driver such as argon-fluoride, which has a native bandwidth in excess of 7 THz. Either of these two options would enable higher and more symmetric ablation pressures in future, direct-drive, ICF target designs.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".