Real-time ultrawide-band group delay profile monitoring through low-noise incoherent temporal interferometry
Bibliographic record
Abstract
A simple, highly accurate measurement technique for real-time monitoring of the group delay (GD) profiles of photonic dispersive devices over ultra-broad spectral bandwidths (e.g. an entire communication wavelength band) is demonstrated. The technique is based on time-domain self-interference of an incoherent light pulse after linear propagation through the device under test, providing a measurement wavelength range as wide as the source spectral bandwidth. Significant enhancement in the signal-to-noise ratio of the self-interference signal has been observed by use of a relatively low-noise incoherent light source as compared with the theoretical estimate for a white-noise light source. This fact combined with the use of balanced photo-detection has allowed us to significantly reduce the number of profiles that need to be averaged to reach a targeted GD measurement accuracy, thus achieving reconstruction of the device GD profile in real time. We report highly-accurate monitoring of (i) the group-delay ripple (GDR) profile of a 10-m long chirped fiber Bragg grating over the full C band (~42 nm), and (ii) the group velocity dispersion (GVD) and dispersion slope (DS) profiles of a ~2-km long dispersion compensating fiber module over an ~72-nm wavelength range, both captured at a 15 frames/s video rate update, with demonstrated standard deviations in the captured GD profiles as low as ~1.6 ps.
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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".