Ultra-Narrow Optical Features Using Polarization-Dependent Gain in Elliptically Birefringent Fibers
Bibliographic record
Abstract
Elliptically birefringent fibers differ from linearly and circularly birefringent fibers because their polarization eigenmodes vary with frequency and differ between forward and backward propagation. Since optical nonlinearities are inherently polarization-dependent, leveraging the properties of these frequency-dependent polarization eigenmodes offers a novel method to control these nonlinearities. Despite extensive research on optical nonlinearities in fibers, no model currently accounts for the frequency dependence of polarization eigenmodes in elliptically birefringent fibers. This thesis introduces a vector model based on the nonlinear Schr¨odinger equations that incorporate the frequency dependence of polarization eigenmodes to analyze optical nonlinearities in unidirectional and bidirectional propagation within elliptically birefringent fibers. The model demonstrates that slight frequency detunings (less than a megahertz) can either enhance or suppress nonlinear interactions between co-propagating and counter-propagating polarization eigenmodes in uniformly twisted elliptically birefringent fibers (also known as spun birefringent fibers). This mechanism enables the creation of tunable ultra-narrow spectral dips in the Brillouin and four-wave mixing gain spectra, with an equivalent Q-factor exceeding 200 million. These ultra-narrow spectral features are unique to elliptically birefringent fibers and are not observed in linearly or circularly birefringent fibers because their polarization eigenmodes are independent of optical frequency. The parameters of these optical features, including linewidth, depth, and spectral location, are tunable in real-time by adjusting the input power and polarization. The ultra-narrow linewidth, ease of implementation, and real-time tunability of these features offer potential applications in microwave photonics, optical signal processing and storage, optical sensing, and optical communication. Additionally, this thesis quantifies the slow/fast light effects using these ultra-narrow spectral features, with a potential optical advancement of up to 0.64 μs using sub-megahertz spectral dips in the Brillouin gain spectrum of spun birefringentfibers. These dips are also highly sensitive to polarization and frequency variations, making them promising for developing sensitive optical sensors and ultra-narrow bandwidth microwave photonic filters.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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 teacher head, 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".