Vlasov–Maxwell simulations of backward Raman amplification of seed pulses in plasmas
Bibliographic record
Abstract
Abstract We study the problem of the amplification of an ultra-short seed pulse via stimulated Raman backscattering (SRB) from a long pump pulse (assumed to have an envelope with a constant amplitude), in an underdense plasma. The SRB interaction couples the pump light wave to a daughter light seed wave propagating in the opposite direction, scattered off an electron plasma wave. In recent numerical simulations, it has been observed that besides stimulated Raman backward scattering (SRBS) and stimulated Raman forward scattering, other high-frequency kinetic instabilities can occur when modified distribution functions exist during the evolution of the system. In particular, we showed the prominent role played by kinetic electrostatic electron nonlinear (KEEN) waves (Afeyan et al., 2004). We continue this work by applying a relativistic Vlasov–Maxwell code to study stimulated KEEN wave scattering (SKEENS) and its role in the SRBS short pulse amplification processes. An analysis of the full spectrum of waves participating in the amplification processes is presented. The absence of spurious noise in grid-based Vlasov codes allows us to follow the evolution of the system with a kinetic (collisionless) description. This affords us a glimpse at the intricate phase-space structures such as trapped particle orbits, which coexist and interact nonlinearly in the electron distribution function.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".