Enhanced pulseshaping capabilities and reduction of non-linear effects in all-fiber MOPA pulsed system
Bibliographic record
Abstract
Pulseshaping is important in high energy pulsed fiber MOPA system to mitigate non-linear effects and optimize the processing of different materials. However, pulseshaping is greatly limited by the spectral features of the semiconductor seed source commonly used as the master oscillator. Through the appropriate design of an external fiber Bragg grating (FBG) and adequate current modulation, the spectrum of the fiber-coupled seed laser was broadened to suppress stimulated Brillouin scattering occurring in the amplifier chain and the central emission wavelength and bandwidth were controlled. Pulseshaping is also quickly limited by the saturation energy and doping level of standard aluminosilicate ytterbium doped fibers used in the power amplifier even with large core diameter. Co-doping the fiber with phosphorus greatly increases the saturation energy of the system, which gives smoother pulseshape and significantly lower stimulated Raman scattering (SRS). It is shown that going from 1060 nm to longer emission wavelength at 1090 nm with this fiber increases further the pulseshaping capabilities and reduces SRS. The phosphorus codoping also allows higher ytterbium doping level without photo-degradation, which decreases nonlinear effects generation during the amplification while giving more flexible pump wavelength choice and efficiency.
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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 | 0.000 |
| 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) | 0.002 | 0.001 |
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".