Numerical modeling of the intracavity stimulated Raman scattering as a source of subnanosecond optical pulses
Bibliographic record
Abstract
We present a computer numerical model (virtual sub-nanosecond laser) utilizing intracavity stimulated Raman scattering. The goal of this work is to shorten laser output pulses (for which the highly nonlinear frequency conversion process stimulated Raman scattering is used) and to obtain high efficiency (which is enhanced by placing a Raman-active crystal inside the cavity where the fundamental laser frequency intensity is maximal). The following laser components were modeled: a diodepumped solidstate laser active medium (a crystal of the Nd<sup>3+</sup>:YLF type), a closed cavity for a wave on its fundamental frequency with a Q-switching element and an internal subcavity with a Ramanactive crystal with controlled output coupler transmission at the Raman frequency. The model components are: a numerical integrator of a set of three rate equations (for an inverse population of the laser medium and for the number of fundamental and Stokes frequency photons), random number sources for radiation seeding, and an interactive data input interface and graphic output. A wide range of parameters was investigated and output pulses as short as 0.8 ns were found. The optimal conditions for the maximal peak power of Stokes pulses were determined and the conditions for generating pulse trains for burst laser machining were identified.
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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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".