In-amplifier soliton self-frequency shift optimization by pre-chirping – experimental demonstration
Bibliographic record
Abstract
Soliton self-frequency shift (SSFS) is a fundamental mechanism of optical wavelength conversion and supercontinuum generation. Often, it is desirable to use a nonlinear propagation design that provides a large amount of SSFS, leading to wavelength conversion with a large frequency offset or leading to a broad supercontinuum generation. The most effective approach to enhance SSFS is using an amplifying medium. In this context, it was theoretically predicted that a pre-amplified seed pulse should be chirped to maximize the extent of SSFS. Here, we make the experimental verification of this claim. For this purpose, a chirped seed pulse at a wavelength of 1880 nm is amplified and experiences SSFS in a Tm 3+ -doped fiber amplifier. The resulting soliton reaches a final wavelength that is tuned by adjusting the energy and chirp of the pre-amplified seed pulse. The experiment demonstrates that SSFS and energy conversion efficiency are maximized when the pre-amplified seed pulse is chirped at C 0 ≈ 0.65 g L D , where g L D is the total gain over one dispersion length. This research provides a fundamental conclusion for optimizing SSFS processes using any amplifying medium and finds applications for large offset wavelength conversion and broadband supercontinuum generation.
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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 | 0.000 |
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".