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Record W2035134369 · doi:10.1117/12.513109

Fast camera objective designs for spectrograph of Mont Megantique telescope

2004· article· en· W2035134369 on OpenAlexaff
Simon Thibault, Min Wang

Bibliographic record

VenueProceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2004
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAdaptive optics and wavefront sensing
Canadian institutionsInstitut National d'Optique
Fundersnot available
KeywordsOpticsSpectrographAchromatic lensPhysicsSpectral resolutionTelescopeLens (geology)Monochromatic colorImage resolutionPixelFocal lengthCardinal pointSpectral line

Abstract

fetched live from OpenAlex

All-reflective optics is conventionally required for extended spectral region observations in astronomical spectrograph. But the spatial resolution is usually not good enough while the large-size CCD will be used for observation in all-reflective optics. In this paper, all-refractive optics has been investigated to design a fast (F/1.55) and wide angle camera objective for large spectral coverage, from UV to VIS and up to NIR, when a large-size CCD is used on the focal plane of the spectrograph of Mont Megantique telescope. The case of achromatic and apochromatic condition has been investigated for axial and lateral color controls. The new proposed solutions have been optimized from two to three different glass combinations in order to have higher throughputs for large spectral coverage, especially in UV region. The used components have been minimized to reduce the light inherent lost. The monochromatic aberrations have been corrected and controlled by using optimized lens bending and shapes to make the camera have the CCD pixel resolution. Ray tracing results displayed the good optical performance of the camera to cover from 350 nm to 1000 nm spectral region with high resolution. The broadband AR coating, enhanced on UV region, will be used on each surface of the lenses in the camera. Final throughputs for the designed camera has been estimated and given in the paper.

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.

How this classification was reachedexpand

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.585
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.011
GPT teacher head0.229
Teacher spread0.218 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2004
Admission routes1
Has abstractyes

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