8.5-fm resonances in an amplified slow-light fiber Bragg grating for high-precision metrology
Bibliographic record
Abstract
Fiber Bragg gratings (FBGs) with strong apodized index modulations behave like an in-line Fabry-Perot interferometer and exhibit a series of narrow resonances in the short-wavelength portion of their transmission spectrum. These resonances have proven invaluable for detecting extremely small strains (30-femtostrain/√Hz level) or temperature changes (millidegreeC/√Hz level). The sensitivity of these fiber sensors is limited by the linewidth and peak transmission of the resonance used to interrogate the sensor, which are themselves limited by the intrinsic loss of the grating. In this work, significantly narrower and stronger resonances are demonstrated by introducing a small amount of optical gain in the FBG to offset the intrinsic loss and create a resonator with a much smaller net internal loss. The fiber Bragg grating is written in an Er-doped single-mode fiber and optically pumped to provide the required gain. The device reported here is a 6.5-mm grating with an AC index modulation of 1.59×10-3. With only 30 μW of pump power absorbed by the grating (32.6 mW launched), the fundamental resonance of the FBG was observed to narrow from 737 fm in the absence of pump to a record linewidth of 8.5 fm. The measured peak transmission of the resonance improved from ~-37 dB to -0.2 dB. A new model that predicts the slow-light resonance spectrum of a slow-light grating in the presence of optical gain is presented. This model is in good quantitative agreement with the measured evolution of the resonance linewidth as the pump power and the power of the laser that probes the resonance lineshape are varied.
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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".