Ultrafast Optical Arbitrary-Order Differentiators Based on Apodized Long-Period Gratings
Bibliographic record
Abstract
We propose a novel, optimized design for arbitrary-order optical differentiation based on a uniform-period especially apodized long-period fiber or waveguide grating (LPG) operated in transmission. We show that the LPG solution can be optimized to utilize the entire grating resonance bandwidth for optical differentiation by properly customizing the LPG apodization profile through a discrete inverse-scattering grating synthesis technique. This strategy leads to a significantly increased processing speed and a maximized energetic efficiency as compared with previous unapodized LPG-based optical differentiator designs. As an example, optimized first-, second-, and third-order optical differentiators are designed using apodized LPGs implemented in standard single-mode fiber (SMF). The designed passive devices are practically feasible and offer an unprecedented operation bandwidth of 12 THz, which is capable of accurately processing time features as short as ~100 fs, and an optimal energetic efficiency, which reaches a peak power spectral response of nearly 100% within their operation band.
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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".