Fiber guided mode dispersion spectroscopy via control of spatial dimensions
Bibliographic record
Abstract
The development of next-generation optical fiber grating devices is strongly influenced by intricate grating design, which must effectively capture guided mode dispersion. Understanding this dispersion is crucial for enhancing measurement accuracy in dispersion compensation, guided mode phase matching for nonlinear frequency conversion, and optical sensing. However, higher-order guided modes remain challenging to interpret due to limited experimental validation. In this study, we present tilted fiber Bragg grating based mode dispersion spectroscopy that allows for real-time tracking of high order guided mode (∼50th order) dispersion as a function of fiber diameter and wavelength. As fiber diameter reduces, all guided mode resonances shift to shorter wavelength, the separation between resonances associated with even and odd azimuthal order increases, and single resonances split into multiple peaks relying on polarization effects and coupling efficiency. Simulations based on coupled-mode theory corroborate these findings, revealing that with reduced fiber diameter, the azimuthal mode order contribute to such unexpected resonance splitting. This investigation represents a significant step forward, offering new insights into high order guided mode dispersion calibration and demonstrating how grating can refine existing models for predicting and controlling the mode dispersion.
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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 | 0.000 |
| 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".