Distributed static and dynamic detection of an acoustic wave in a Brillouin random fiber laser
Bibliographic record
Abstract
The interaction of random laser and gain medium is important to understand the noise origin in random fiber lasers. Here, using the optical time domain reflectometry method, the time-resolved distributed acoustic wave generated by a Brillouin random fiber laser (BRFL) is characterized. The dynamic property of the acoustic wave reflects the gain dynamics of the BRFL. The principle is based on the polarization-decoupled stimulated Brillouin scattering (SBS)-enhanced four-wave mixing process, where the probe light experiences maximum reflection when the phase match condition is satisfied. Static measurements present exponentially depleted Brillouin gain along the gain medium in the BRFL, indicating the localized random SBS frequency change in the maximum local gain region, which varies with time to contribute random laser noise as revealed in the dynamic measurement. The SBS-induced birefringence change in the Brillouin gain fiber is approximately 10 − 7 to 10 − 6 . The phase noise of the BRFL is observed directly inside the random laser gain medium for the first time via time and spatially varied acoustic wave intensity. By counting the temporal intensity statistical distribution, optical rogue waves are detected near the lasing threshold of the BRFL. Different temporal intensity statistical distribution at high and low gain positions is found, which is caused by the SBS nonlinear transfer function and localized gain. The distributed characterization methods in the paper provide a new platform to study the interaction of random lasers and gain medium, giving us a new perspective to understand the fundamental physics of the random lasing process and its noise property.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".