Demonstration of standing cavity Brillouin random fiber lasers using double fiber Bragg grating arrays
Bibliographic record
Abstract
Bidirectional feedback by fiber Bragg grating arrays (FBGAs) reduced the loss of the cavity and increased stimulated Brillouin scattering (SBS) gain by bi-directional Stokes wave through FBGA associated Rayleigh feedback of the pump wave. As a result, the Q value of the Brillouin random fiber laser (BRFL) increased significantly, which leads to narrow linewidth. This is different from the ring configuration with unidirectional SBS gain versus dual SBS gain of the same fiber length. Highly efficient use of the SBS gain fiber for coherent SBS amplification suppressed thermal noise associated Stokes wave. Such an efficient SBS laser is realized by a standing cavity BRFL based on double FBGAs. Multiple scattering of light traveling in strong scattering FBGAs enables light localization and the generation of high-Q reflection peaks. Coherent SBS amplification with high Q help to reduce laser relative intensity noise (RIN) and laser linewidth. Experimental results demonstrate that the BRFL supports localized modes by increasing the scattering strength of the FBGA random feedback, resulting in long lifetime and single-frequency emission with 20 dB noise floor reduction. The BRFL with a 1 km Brillouin gain fiber exhibits lower RIN and narrower linewidth than that with a 10 km Brillouin gain fiber due to the stronger gain competition of more modes in the longer cavity length. The optimized standing caivty BRFL with 1 km gain fiber leads to 3.5 kHz linewidth versus 40 kHz from the pump laser. These findings provide experimental evidence that double FBGAs offer a unique setting to control mode dynamics, realizing low-noise single-frequency lasing.
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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".