Multiwavelength Coherent Brillouin Random Fiber Laser With Ultrahigh Optical Signal-to-Noise Ratio
Bibliographic record
Abstract
A narrow-linewidth multiwavelength Brillouin random fiber laser (MW-BRFL) was experimentally demonstrated and characterized, which was achieved by mutually combining Brillouin amplification with randomly distributed Rayleigh feedback in optical fibers. By cascading process through a sub-fiber loop, up to six orders Stokes emissions simultaneously resonate in a coherent lasing fashion under the acoustic coupling of stimulated Brillouin scattering in the presence of coherent Rayleigh scattered random feedback, which acts as an optical filter with the high reflection coefficient for lowest order longitudinal mode of each Stokes line to ensure single longitudinal mode operation. Consequently, random laser output with an unprecedented ultra-high optical signal-to-noise ratio of ~47 dB and an optimal peak power discrepancy of 1.8 dB were obtained. Furthermore, each Stokes random lasing emission with narrow linewidth of ~1 kHz was achieved thanks to coherent random lasing. Relative intensity noise transfer was observed in sequence from the first-to sixth-order Stokes emission. In this MW-BRFL, a wide tunable range over 30 nm of the operating wavelength was validated by shifting the central wavelength of the input pump from 1530 to 1560 nm. Meanwhile, the statistical properties of the MW-BRFL and the performance optimization were also experimentally investigated.
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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 | 0.001 | 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".