Adaptive null test system using a ferrofluid deformable mirror
Bibliographic record
Abstract
With the growing number of complex-shaped lenses, aspheric and freeform surfaces, the demand for an appropriate and cost effective measurement technique to test these high quality components is still very high. Ferrofluid deformable mirrors (FDMs) offer a promising alternative. However, high accuracy profiles produced by FDMs have only been demonstrated in a closed-loop system which is inappropriate for metrology applications as it requires an additional measurement instrument and complicates the setup. Consequently, a FDM open-loop driving technique which maintains good precision while being simple, robust and stable, is required. In the following paper, we present a new active null test system based on a FDM for the testing of deep aspheric surfaces. We show a new driving method which provides an accurate open-loop operation mode of a FDM. We demonstrate that the method gives a significant improvement in comparison with the normalized influence function method. The results are promising enough to consider an active null test configuration for measuring optical components having high sag departures or complicated continuous profiles.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".