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Record W4319594495 · doi:10.1117/1.oe.62.2.025102

Stray light analysis of diamond-turned image slicers

2023· article· en· W4319594495 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

VenueOptical Engineering · 2023
Typearticle
Languageen
FieldPhysics and Astronomy
TopicRandom lasers and scattering media
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsStray lightOpticsComputer scienceProcess (computing)Fourier transformDiffractionImage formationLight scatteringScatteringPhysicsImage (mathematics)Computer vision

Abstract

fetched live from OpenAlex

We describe the process through which stray light analysis should be performed in optical systems involving image slicers. We detail how scattering models should be used depending on how the image slicer assembly will be fabricated. Our work describes how to determine all ray paths, compute cross-talk on the pupil mirrors due to scattering, quantify the ghost images’ intensity, and determine baffle positions. In the example given, an ABg model is applied to all mirror arrays separately, before considering their contributions altogether. We also consider diffraction due to the image slicer’s narrow slice apertures, which contribute to unwanted light in the system by causing cross-talk on the pupil mirrors. Using Fourier optics, this quantity is computed and compared with cross-talk caused by scattering. Our work represents a useful asset for optical engineers who work on image slicer-based systems and want to analyze stray light, by providing a clear and exhaustive procedure to follow to obtain accurate estimates.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.487
Threshold uncertainty score0.363

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.004
GPT teacher head0.210
Teacher spread0.206 · 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