Multimode optical fiber Bragg gratings: modeling, simulation, and experiments
Bibliographic record
Abstract
Fiber Bragg gratings (FBGs) have emerged as important components and received intensive research attention in both fiber telecommunication and sensing fields. Bragg gratings in single mode fiber structure (SMFBGs) have been studied extensively. On the other hand, fewer studies have been reported on multimode fiber Bragg gratings (MMFBGs) despite of their potential applications in future optical access networks. In this paper, MMFBGs are studied in detail both theoretically and experimentally. A comprehensive numerical model is developed for MMFBGs based on the coupled mode theory and applied to analyze measured transmission and reflection spectra from MMFBGs with reflectivities ranging from 78% to 99%. It is found that the spectra of MMFBGs depend strongly on fabrication conditions (e.g. modulation depth of the grating) and experimental conditions (e.g. mode excitation). Good agreement is obtained between the theoretical simulations and experimental measurements. Our simulations based on the developed MMFGB numerical model can provide quantitative explanations for the observed experimental phenomena. These explanations give a complete understanding of the nature of the interaction between the wave propagation and multimode fiber gratings. It is considered that the spectral simulations provide a theoretical guidance in design MMFBG based devices.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 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".