Dynamic detection of acoustic wave generated by polarization maintaining Brillouin random fiber laser
Bibliographic record
Abstract
The intrinsic spectral width and intensity dynamics of the acoustic wave generated by the Brillouin random fiber laser were characterized experimentally for the first time. These are important to the understanding of the dynamic noise properties of random fiber lasers based on stimulated Brillouin scattering. We demonstrate that the spectra of the acoustic wave in the gain medium are determined by the spectral convolution of pump light and its Stokes light, which is further an influential factor on the narrow linewidth of the random fiber laser resulting from the distributed feedback of coherent Rayleigh scattering. The power of probe light is weak to ensure the minimum disturbance to the random fiber laser. In the time domain, the intensity of the reflected probe light exhibits a similar output property to that the Brillouin random fiber laser with Gaussian probability distribution when both narrow linewidth lasers (on the order of several kHz) are used as pump light and probe light. In contrast, stochastic noise features with an exponential probability distribution are introduced to the intensity dynamics of the reflected probe light when the linewidth of pump light or probe light is several MHz. The phase noise and intensity noise of the reflected probe light prove that acoustic wave generation and detection is based on a four wave mixing process, which enhances our understanding of the wave coupling in random fiber lasers and gives 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 | 0.001 |
| 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".