Universal photonic processor for spatial mode decomposition
Bibliographic record
Abstract
Efficient and precise information storage and processing using light’s various degrees of freedom - intensity, phase, and polarization - have vast applications in modern photonics. The corresponding utilization necessitates the accurate measurement and decomposition of arbitrary spatial modes into their orthogonal components. In this paper, we introduce a new modal decomposition technique based on a 16-pixel reconfigurable photonic integrated circuit programmed as a spatial mode decomposer. This device uniquely identifies and quantifies the relative contributions of constituent modes in a Laguerre-Gaussian basis. The presented device not only provides the relative weights of these modes but also their relative phases, offering a novel approach based on an integrated platform for optical information processing. We further highlight a novel input interface that enables the decomposition of input beam polarization into circular polarization basis. The potential applications of this technology are vast, ranging from advanced optical communications to microscopy and beyond, marking a significant stride in the field of integrated photonics. A reconfigurable photonic integrated circuit for modal decomposition in the Laguerre-Gaussian basis is introduced. The device measures relative phase, amplitude, and partial polarization of the constituting modes. It is capable of distinguishing up to 9 modes, providing a compact next generation platform for beam metrology.
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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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".