Optical delay line using rotating rhombic prisms
Bibliographic record
Abstract
We propose a robust and efficient delay line using an ensemble of rotating rhombic prisms. Delay lines relying on rotating elements provide fast and stable operation. Optical systems using rhombic prisms are quite easy to align since these prisms are efficient even when slightly misaligned. Optical delay lines with a single rotating element usually have a poor duty cycle and show large nonlinearity in the variation of the optical path lengh with the angular position. Our delay line improves over existing technology by using off-centroid rotation and reinjection. Off-centroid rotation allows the use of multiple prisms and, by optimizing the conditions of operation, the duty cycle is increased and the nonlinearity is decreased. The duty cycle and repetition rate are further increased by reinjecting the incoming ray towards the delay line when it is not first intercepted by the prism ensemble. We have designed and built such a delay line using five prisms. The experimental device was tested at 2000 delay scans per second and provided a duty cycle larger than 80% with about 5% nonlinearity. Higher delay scan rates are easily achievable with this technology. The delay line was introduced in a time-domain optical coherence tomography system and example of imaging of biological tissue is provided.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".