Suppressing parametric instabilities in direct-drive inertial-confinement-fusion plasmas using broadband laser light
Bibliographic record
Abstract
It has long been recognized that broadband laser light has the potential to control parametric instabilities in inertial-confinement-fusion (ICF) plasmas. Here, we use results from laser-plasma-interaction simulations to estimate the bandwidth requirements for mitigating the three predominant classes of instabilities in direct-drive ICF implosions: cross-beam energy transfer (CBET), two-plasmon decay (TPD), and stimulated Raman scattering (SRS). We find that for frequency-tripled, Nd:glass laser light, a bandwidth of 8.5 THz can significantly increase laser absorption by suppressing CBET, while ∼13 THz is needed to mitigate absolute TPD and SRS on an ignition-scale platform. None of the glass lasers used in contemporary ICF experiments, however, possess a bandwidth greater than 1 THz and reaching larger values requires the use of an auxiliary broadening technique such as optical parametric amplification or stimulated-rotational-Raman scattering. An arguably superior approach is the adoption of an argon-fluoride (ArF) laser as an ICF driver. Besides having a broad bandwidth of ∼10 THz, the ArF laser also possesses the shortest wavelength (193 nm) that can scale to the high energy/power required for ICF—a feature that helps to mitigate parametric instabilities even further. We show that these native properties of ArF laser light are sufficient to eliminate nearly all CBET scattering in a direct-drive target and also raise absolute TPD and SRS thresholds well above those for broadband glass lasers. The effective control of parametric instabilities with broad bandwidth is potentially a “game changer” in ICF because it would enable higher laser intensities and ablation pressures in future target designs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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) | 0.000 | 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".