The use of low departure aspheric surfaces in high quality wide angle lenses
Bibliographic record
Abstract
Modern lens designs for digital sensors, such as those required in medium volumes for cinematography, often require the use of one or two high departure aspheric surfaces. With departures from best fit sphere of up to a few millimeters, the use of such surfaces are accompanied by a number of consequences: high cost metrology, very tight opto-mechanical tolerances and image artifacts due to the sub-aperture grinding and polishing process. Previously we examined the use of multiple aspheric surfaces with very low departures from best fit sphere (BFS) and concluded that advantages may be gained in standard and telephoto lenses, but not in wide angle lens designs1. In this work we consider the potential benefits of low departure aspheric surfaces, as applied to wide angle lenses in particular. We review the number, placement, and nature of aspheric surfaces in some wide angle lens design examples, and look at the potential to redesign with an increased number of low departure aspheric surfaces that have the potential to be manufactured without the need for computer generated holograms (CGH’s). The use and limitations of modern interferometers capable of measuring aspheric surfaces without the use of CGH’s will be considered. In one example we examine the performance, manufacturing, and cost perspective, paying particular attention to testing and mechanical alignment tolerances.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".