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Record W1985916126 · doi:10.1117/12.552936

Optical design of PANORAMIX-II, the OMM wide-field VIS-NIR camera

2004· article· en· W1985916126 on OpenAlexaffabout
Simon Thibault, Michel Doucet, Laurent Drissen

Bibliographic record

VenueProceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2004
Typearticle
Languageen
FieldEngineering
TopicInfrared Target Detection Methodologies
Canadian institutionsUniversité LavalInstitut National d'Optique
Fundersnot available
KeywordsOpticsCardinal pointPhysicsDistortion (music)Field of viewCassegrain reflectorPixelOptical filterDetectorLarge formatFocal lengthSkyFilter (signal processing)Computer scienceOptoelectronicsLens (geology)Computer visionAstronomyTelescope

Abstract

fetched live from OpenAlex

In this paper, we present the optical design of the optical train of Panoramix-II, a wide-field VIS-NIR camera to be installed at the cassegrain focus of OMM (Observatoire du Mont Megnantic, Quebec, Canada). This camera is optimized for g (0.41-0.55), r (0.556-0.689), i (0.693-0.867) and z (0.851-0.95) operating region and used a 2kX4k EEV detector. The sky will be imaged onto the focal plane at an optical speed of F/2.35 yielding an image scale of 0.75 arcsecond per 13.5 μm pixel. The design image quality is 0.75 arcsecond 50% diffraction encircled energy over the central 27 arcmin field and no images worse than 0.85 arcsecond over the 55 arcminute diameter camera field. The optical design distortion at the corners is less than 0.08%. The Panoramix-II camera have a set of filters is used at the internal pupil. The image of the pupil is sufficiently sharp to limit the filter size. We discuss the principle characteristics of the imager, the specifications, the optical design, the performances, a ghost study and finally a tolerance anlaysis.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.009
Threshold uncertainty score0.029

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0090.003

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 head0.021
GPT teacher head0.243
Teacher spread0.222 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreMethods

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 routes2
Has abstractyes

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Same venueProceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIESame topicInfrared Target Detection MethodologiesFrench-language works237,207