Single-longitudinal mode quadruple wavelength C+L-band erbium-doped fiber laser based on the pairs of reflective fiber bragg gratings
Bibliographic record
Abstract
Abstract Flatness of lasing wavelengths of a multi-wavelength fiber laser (MWFL) is an important design constraint that is considered attractive in various applications of photonics and optical communication systems. In this work, we propose a single-longitudinal mode (SLM) quadruple wavelength C+L-band Erbium-doped fiber laser (EDFL) with high output power flatness. It is implemented with a short piece of Erbium-doped fiber (EDF) pumped by conventional 980 nm laser diode and four pairs of reflective fiber Bragg gratings (FBGs) through numerical simulations. The reflectivities of FBGs are adjusted such that SLM quadruple wavelengths are obtained at the output of EDFL with flatness of 0.9 dB, optical signal-to-noise ratio (OSNR) in the range of 44.5–53.2 dB, and linewidths (LWs) in the range of 5.4–7.3 MHz. Slope efficiency (SE) of around 24% is achieved considering the total power of all lasing wavelengths generated. Moreover, the power variation around 0.1 dB for lasing wavelengths of 1540.2 nm and 1605.7 nm is noticed for twelve iterations each repeating after five-minute interval. The proposed SLM quadruple wavelength EDFL has promising application prospects for distributed sensing and optical communication systems due to excellent performance metrics.
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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) | 0.000 | 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 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".