On the SBS threshold of optical pulse shapes compensated for gain saturation
Bibliographic record
Abstract
Nanosecond pulsed fiber laser sources have found multiple usages in material processing applications. Their reliability, flexibility, low cost, high average power and high beam quality are the reasons for their commercial success. With appropriate means, nanosecond fiber lasers based on a MOPA configuration can emit pulses with tailored shapes. This feature greatly increases the flexibility of the laser as it allows the emission of pulses of adjustable duration, complex pulse shapes such as bursts of short pulses, gain saturation-compensated shapes, chair-like shapes, or any other variations. Pulse shaping can have a significant impact on the ablation rate, the surface quality of the processed sample and different materials have been shown to respond differently to those pulse shape variations (the thermal conductivity of the material being a key parameter). Pulse shaping is also very valuable for optimizing the pulse energy from a system as it allows pre-compensation for the pulse distortion caused by gain saturation which tends to narrow the pulse duration, increase the peak power and associated SRS sensitivity. Through pulse shaping, one can achieve pulse energies that are significantly higher than the saturation energy of the amplifier while mitigating the detrimental effects of SRS. It is however important to consider the impact of pulse shape and pulse duration on the SBS threshold of an optical system. We have performed numerous SBS threshold measurements for pulses of varying duration and of varying levels of precompensation for gain saturation. We have demonstrated that pulse shapes with effective pulse duration of 40 ns have the lowest SBS threshold.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 teacher head, 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".