Perfect Vortex Modes for Nondestructive Characterization of Mode Dependent Loss in Ring Core Fibers
Bibliographic record
Abstract
Ring core fibers (RCF) enable high-performance modal multiplexing with low crosstalk and can support orbital angular momentum (OAM) modes. RCFs are challenging to characterize due to the lack of commercial multiplexers, especially for high OAM orders. For fibers supporting large numbers of modes, typical cutback techniques for characterization are extremely wasteful of fiber, especially as one cutback is required for each mode. We show the differential modal loss across modes 3 to 10 was significantly underestimated using an OTDR when exciting modes individually or when exciting all modes indiscriminately. We exploit perfect vortex beams to achieve reliable and nondestructive characterization of mode-dependent loss (MDL) for OAM modes. Perfect vortex beams allow us to maximize the coupling efficiency at each mode launch, increasing the accuracy of MDL estimate. We fabricated fiber with a refractive index difference between the ring core and the cladding of a$5.1\times 10^{-2}$. For this fiber, mode orders 3 to 10 are the most suitable for data transmission and were the focus of our work (the fiber support up to OAM order 13). Such a high index difference can lead to MDL. We demonstrate that the modal loss spans from 2.14 to 4.38 dB/km for orders 3 to 10.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".