Divide and focus: generating novel focal polarization modalities by symmetry-based phase tailoring in one dimension
Bibliographic record
Abstract
Symmetry-based tailoring of photonic systems recently heralded the advent of novel concepts, such as photonic topological insulators and bound states in the continuum. In optical microscopy systems, similar tailoring was shown to result in tighter focusing, spawning the field of phase- and polarization-tailored light. Here, we show that even in the fundamental case of 1D focusing using a cylindrical lens, symmetry-based phase tailoring of the input field can result in novel features. Dividing the beam or utilizing a π phase shift for half the input light along the non-invariant focusing direction, these features include a transverse dark focal line and a longitudinally polarized on-axis sheet. While the former can be used in dark-field light-sheet microscopy, the latter, similar to the case of a radially polarized beam focused by a spherical lens, results in a z polarized sheet with reduced lateral size when compared with the thickness of a transversely polarized sheet produced by focusing a non-tailored beam. Moreover, the switching between these two modalities is achieved by a direct 90° rotation of the incoming linear polarization. We interpret these findings in terms of the requirement to adapt the symmetry of the incoming polarization state to match the symmetry of the focusing element. The proposed scheme may find application in microscopy, probing anisotropic media, laser machining, particle manipulation, and novel sensor concepts.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".