Highly Sensitive Polarization Rotation Measurement through a High‐Order Vector Beam Generated by a Metasurface
Bibliographic record
Abstract
Abstract The precise determination of the polarization state of light is fundamental for a vast variety of applications in remote sensing, astronomy, optics and terahertz technology, to name just a few. Typically, polarization characterization is performed by using a combination of multiple optical devices such as beam splitters, polarizers, and waveplates. Moreover, to achieve high‐precision, balanced photodetectors and lock‐in amplifiers are employed, thus contributing to increasing system complexity. Here, a technique for polarization rotation measurements with a dynamic range of 180° and a sensitivity of about 10−2 degrees is realized using a properly designed metasurface. Such device generates a vector beam with an azimuthally‐dependent polarization distribution, as a result of the superposition of two vortex beams carrying opposite orbital angular momenta (ℓ = ±30). After propagation through a linear polarizer, the spatial intensity profile of such a beam turns into 60 lobes. By tracking the displacement of only two of these lobes on a camera, the rotation of the input polarization state can be retrieved with high resolution. The proposed approach offers a new route toward the development of compact high‐precision polarimeters and can also be exploited in quantum information processing, optical communications, as well as nonlinear and chiral optics.
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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) | 0.001 | 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".