Dispersion characteristics of fiber Bragg gratings with Gaussian self apodization made with a femtosecond laser in heavily doped Erbium and Ytterbium fibers
Bibliographic record
Abstract
Short fiber lasers are increasingly studied due to their applications in communications and sensing1. These lasers require high concentrations of Erbium (Er) and Ytterbium (Yb) that are not compatible with the presence of Germanium (Ge) in the fiber core2. In stark contrast with more conventional fabrication methods, ultrafast lasers now allow for grating inscription within fibers having no Ge doping3. Normally for short gratings the reflected signal dispersion is small and relatively harmless to the operation of long cavities. As cavity length decreases however the signal will tend to travel more and more within the gratings, interacting with them proportionately more often. Hence a thorough understanding of the grating dispersion characteristics becomes even more important. As a result of their physical differences, the characteristics of ultrafast gratings can vary substantially from those produced using more conventional fabrication methods, and it is unknown whether these factors in combination with a high dopant concentration will significantly affect the dispersion properties of such gratings. In this study, Bragg gratings made with infrared (IR) femtosecond radiation and a first order phase mask were inscribed in fibers heavily doped with Er and Yb as well as a pure silica core fiber. Subsequent measurements of the power spectra, group delay and group delay ripple (GDR) are reported herein.
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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".