Synthesis of Fiber Bragg Gratings With Arbitrary Stationary Power/Field Distribution
Bibliographic record
Abstract
A method to synthesize a fiber Bragg grating (FBG) providing a desired, arbitrary stationary power/field distribution along the grating length is proposed and numerically demonstrated. In the proposed method, starting from the desired stationary power/field distribution or its differential at the Bragg wavelength, the forward and the backward propagation modes are derived for a uniform-period FBG consisting of a prescribed number of grating sections. Using the transfer matrix method, the local reflection coefficient (i.e., ρk) is calculated from the two derived propagation modes, and this information is subsequently employed to obtain the corresponding local coupling coefficient (i.e., qk) and the associated refractive index modulation (i.e., FBG profile) along the grating length. The proposed synthesis method is first numerically verified when two stationary power distributions from a uniform and a Gaussian-apodized FBGs are chosen as the target, showing an excellent agreement between the reconstructed refractive index modulations and those of the original FBGs. Next, several FBG profiles providing user-defined stationary power distributions, including a flat-top shape, a sharp peak, a saddle shape, or multiple peaks in the power distribution differential, are successfully synthesized. In addition, the proposed method is also shown to be suitable for the synthesis of integrated-waveguide Bragg gratings with arbitrary stationary power/field distributions.
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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".