High sensitivity long-period grating tunable filters in standard communication fibers and their PDL reduction
Bibliographic record
Abstract
Long-period fiber gratings (LPFG’s) find applications in optical fiber communication systems and fiber sensor systems. Among others, it can be used as gain flattening filters (GFF’s) in the communication systems. Depend on the amplifier design, the GFF’s need to be either athermal, or have specific temperature sensitivities. The temperature sensitivity requiement sometimes can be very stringent. It has been known that the temperature and strain sensitivity are dependent on the fiber parameters and the order of the cladding modes it is used. In this paper we will describe the general method for finding suitable cladding mode in a specific fiber for specific requirements. We found that the polarization dependent losses (PDL) in high sensitivity modes are remarkably higher than the ones in common LPFG’s. In those high sensitivity filters achieved by the UV-beam side illuminating, the birefringence-related resonant wavelength separation (RWS), which is the central wavelength separation corresponding the slow and fast axis state of polarizations (SOP), can be in the range of 5 nm, which is remarkably larger than the reported values in other LPFG’s. There are two sources of birefringence which lead to PDL: fiber core ovality induced birefringence, which is intrinsic, and the anisotropic UV-beam exposure induced birefringence. We proposed methods to deal with those birefringence sources. The leads to almost complete remove of the RWS in the high sensitivity LPFG’s.
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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 | 0.001 |
| Open science | 0.000 | 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".