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Record W1847734192 · doi:10.1117/12.2191324

The use of low departure aspheric surfaces in high quality wide angle lenses

2015· article· en· W1847734192 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueProceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2015
Typearticle
Languageen
FieldEngineering
TopicAdvanced optical system design
Canadian institutionsRaytheon Technologies (Canada)
Fundersnot available
KeywordsQuality (philosophy)OpticsMaterials scienceComputer sciencePhysics

Abstract

fetched live from OpenAlex

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 designs<sup>1</sup>. 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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.436
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.029
GPT teacher head0.244
Teacher spread0.215 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it