Spectrally tunable high-power Yb:fiber chirped-pulse amplifier
Bibliographic record
Abstract
Tailoring the properties of the driving laser to the need of applications often requires compromises among laser stability, high peak and average power levels, pulse duration, and spectral bandwidth. For instance, spectroscopy with optical frequency combs in the extreme/visible ultraviolet spectral region requires a high peak power of the near-IR driving laser, and therefore high average power, pulse duration of a few tens of fs, and maximal available spectral bandwidth. Contrarily, the parametric conversion efficiency is higher for pulses with a duration in the 100-fs range due to temporal walk-off and coating limitations. Here we suggest an approach to adjust the spectral characteristics of high-power chirped-pulse amplification (CPA) to the requirements of different nonlinear frequency converters while preserving the low-phase-noise (PN) properties of the system. To achieve spectral tunability, we installed a mechanical spectral shaper in a free-space section of the stretcher of an in-house-developed ytterbium-fiber-based CPA system. The CPA system delivers 100 W of average power at a repetition rate of 132.4 MHz. While gaining control over the spectral properties, we preserve the relative-intensity-noise and PN properties of the system. The high-power CPA can easily be adjusted to deliver either a spectrum ideal for mid-IR light generation (full width at half maximum of ∼ 11 nm , compressed pulse duration of 230 fs) or a spectrum ideal for highly nonlinear processes such as high-harmonic generation ( - 10 dB level of > 50 nm , transform-limited pulse duration of ∼ 65 fs ).
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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 | 0.001 | 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".