Spectral self-imaging phenomena in sampled Bragg gratings
Bibliographic record
Abstract
We present a detailed study of spectral self-imaging phenomena, namely, integer and fractional Talbot effects, observed in the reflection response of chirped sampled fiber Bragg gratings (C-SFBGs). The basic condition for observing spectral self-imaging effects is first derived heuristically, and an intuitive interpretation of the problem based on the notion of multislit interference is also provided. We then present a rigorous analysis of the spectral self-imaging problem in C-SFBGs, including the formal derivation of the conditions for observing the different spectral Talbot effects. This analysis reveals the existence of new effects, in particular, inverse integer and fractional self-images, which are described here for the first time, to our knowledge. Moreover, we also show that the grating physical parameters need to satisfy additional conditions in order for one to be able to observe spectral self-imaging phenomena in C-SFBGs. We also evaluate the impact of deviations from these ideal conditions on the reflection spectrum of a real device. We confirm our theoretical predictions by using numerical simulations, and we report the first experimental observation of fractional spectral Talbot effects in C-SFBGs. Besides their intrinsic physical interest, the results presented constitute the basis for exploiting the spectral self-imaging effect for practical applications, e.g., to optimize the design of SFBGs for applications requiring periodic comb filters with low in-band dispersion.
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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 | 0.001 |
| 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".