Mode characteristic manipulation of random feedback interferometers in Brillouin random fiber laser
Bibliographic record
Abstract
The Brillouin random fiber laser (BRFL) suffers from high intensity noise that comes mainly from longitudinal mode beating at different mode frequencies. In this Letter, we propose and demonstrate that the mode characteristic of BRFL can be manipulated by distributed random feedback, which acts as the longitudinal mode filter. A theoretical model is developed for the first time, to the best of our knowledge, to analyze the mode characteristics of BRFL with different lengths of a weak fiber Bragg grating (FBG) array. In experiment, a single FBG, weak FBG array (reflection of $ - {40}\;{\rm dB}$-40dB) at various lengths, and a Rayleigh scattering fiber are used to provide the random feedback. Both theoretical analysis and experimental results show that single longitudinal mode operation can be realized with the distributed random feedback interferometer, leading to a stable temporal intensity output of the BRFL in the time domain.
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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) | 0.000 | 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".