Intrinsic properties of the optical coupling phenomenon: an experimental investigation using single-mode device and fiber
Bibliographic record
Abstract
High-speed transmission optical devices often use a single-mode fiber as receiver. The fiber must be accurately aligned over five degrees of freedom with respect to an optical field. In previous works, it has been demonstrated that the optical coupling between axisymmetric Gaussian beams has distinctive parabolic, hyperbolic, and linear like characteristics that may be advantageously used to design automated alignment strategies. These properties were proven to exist experimentally when two single-mode fibers are used as receiver and emitter. This paper presents an experimental investigation of these properties for a practical optical coupling situation: the alignment of a receiving single-mode fiber with an optical device. For the purpose of this investigation, the optical device is comprised of an emitting fiber and two lenses mounted in series in order to form a converging optical field at the output of the second lense. A five axis nanopositionning system is used to move the receiving fiber relatively to the device. Even though optical fields are not exactly Gaussian, experiments demonstrate the existence of the properties within a practical range of interest for single-mode device-to-fiber alignment automation. These properties of the coupled optical power provide a strong basis to develop model-based algorithms for axisymmetric single-mode device-to-fiber alignment automation.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".