Orbital Angular Momentum Holography Using Neural Network and Camera in the Loop
Bibliographic record
Abstract
Abstract Orbital Angular Momentum (OAM), a spatial mode of light, is employed as an information carrier for encryption and multiplexing in optical communication and holography. Conventional methods for designing OAM‐multiplexed holograms exhibit suboptimal reconstruction accuracy as the number of multiplexed channels exceeds the nominal multiplexing capacity. Reconstruction quality further degrades in practical systems due to optical aberrations and imperfections in holographic displays. In this work, two methods are proposed, a Gradient Descent (GD) optimization and a deep learning approach, for designing OAM‐multiplexed phase‐only holograms. Both methods are integrated with a Camera‐In‐The‐Loop (CITL) calibration technique that learns a realistic parameterized propagation model to compensate for system imperfections. The experimental results show that when operating at twice the nominal OAM multiplexing capacity, the proposed GD and neural network methods combined with CITL calibration, reduce cross‐correlation errors in reconstructed images by up to 82% and 58% compared to the conventional method, respectively. These methods enable accurate, high capacity, and real‐time OAM‐multiplexed holography in practical optical systems.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".