Mode-Selective Photonic Lanterns With Double-Clad Fibers
Bibliographic record
Abstract
We present the design, fabrication, and characterization of mode-selective photonic lanterns using double-clad fibers. Here, we exploited several custom-pulled double-clad fibers to achieve the symmetry break required to excite higher-order modes. The resulting components are short and exhibit high modal isolation and low excess loss. They address some of the limitations of existing photonic lanterns in terms of fragility and coupling efficiency. The fabrication process involves the use of lower-index capillary tubes to maintain fiber geometry during fusion and tapering. Through the use of varying first cladding diameters, mode selectivity is achieved without sacrificing single-mode compatibility. This in turn allows proper real-time characterization during the whole fabrication process. Results demonstrate that double-clad fibers stacked inside a fluorine-doped capillary tube feature high modal isolation (above 60 dB) and low excess loss (lower than 0.49 dB), over a broad wavelength range (more than 250 nm) with steeper taper profiles, and more robust components. The use of less expensive synthetic fused silica capillary tubes achieves high modal isolation (above 20 dB) and excess loss lower than 2 dB over the same broad wavelength range.
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 | 0.000 | 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".