Optical frequency-dependent opposite effective acoustic velocity dispersion in stimulated Brillouin scattering
Bibliographic record
Abstract
Optical fibers serve not only as optical waveguides but also as acoustic waveguides, a dual functionality stemming from their structural properties and further enhanced by doping techniques. The effective velocity dispersion refers to the variation of acoustic mode velocity as a function of acoustic wavelength or frequency. In the process of stimulated Brillouin scattering (SBS), it has long been considered that the effective velocity dispersion of all excited acoustic modes follows the same trend as each other in a fiber, primarily determined by acoustic waveguide dispersion. However, our recent study, based on accurate measurements of group/effective refractive indices and acoustic frequencies, reveals that the first two acoustic modes in a dispersion-shifted fiber exhibit opposite dispersion. In particular, over the pump wavelength range from 1535 to 1590 nm, the effective acoustic velocity of the fundamental mode decreases by 1.7 m/s as the acoustic frequency lowers, while the second mode increases by 3.1 m/s. They are in strong agreement with the predictions of our model considering anomalous acoustic dispersion. A similar phenomenon is also observed in highly nonlinear fiber. Our finding highlights the necessity of considering acoustic material dispersion in such dynamic processes, providing a deeper understanding of acoustic wave behavior during the SBS process in optical fibers. This insight can enhance optical fiber-based applications and contribute to the improved design of integrated photonic platforms.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".