Transport properties of nonlinear photonic crystals
Bibliographic record
Abstract
Keen interest has been shown in exploiting the transport properties of nonlinear photonic crystals for modulation, switching and routing applications at telecommunication frequencies. This is due to the rich anisotropy evidenced by a highly-textured dispersion surface, and its dependence on the permittivity profile of the structure. We consider a two-dimensional photonic crystal, presume an instantaneous change in profile due to an optical Kerr effect, and show how the beam's refraction angle depends on its intensity. For computational simplicity, we employ a self-consistent approach and the linear eigenvalue equation, which we solve using a finite element method. Previous studies have extracted directions of optical power flow from equifrequency contour gradients. Such gradients, more formally the group velocity, lack local definition and are only meaningful in the context of a spatial average over the unit cell. As a result, the relationship between the local character of the optical transport and the induced permittivity profile is obscured. By contrast, we explicitly consider the spatial dependence of the Poynting's vector, from which we also extract mean transport directions with much greater computational efficiency. We thereby demonstrate the interrelationship between optical transport and nonlinear response at the nanoscale. A consequent analysis of refraction in the context of the superprism effect reveals new aspects to the optical transport in such nonlinear systems.
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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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".