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Record W2037512712 · doi:10.1117/12.855976

The optical design of wide integral field infrared spectrograph

2010· article· en· W2037512712 on OpenAlexaff
Richard C. Y. Chou, Dae‐Sik Moon, Stephen S. Eikenberry

Bibliographic record

VenueProceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2010
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAstronomy and Astrophysical Research
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsSpectrographCollimatorOpticsPhysicsIntegral field spectrographTelescopeField of viewInfraredSpectral resolutionDetectorAstronomySpectral line

Abstract

fetched live from OpenAlex

We present the optical design of the Wide Integral Field Infrared Spectrograph (WIFIS) which provides an unprecedented combination of the integral field size and the spectral resolving power in the near-infrared wavebands. The integral field size and spectral resolving power of WIFIS are ~ 5× 12on a 10-m telescope (or equivalently 13× 30on a 4-m telescope) and ~ 5300, respectively. Therefore, the affordable etendue of WIFIS is larger than any other near-infrared integral field spectrographs while its spectral resolving power is comparable to the highest value provided by other spectrographs. WIFIS optical system comprises an Offner relay-based pre-slit unit, an image slicer for integral-field unit, a collimator, diffraction gratings, and a spectrograph camera. For the integral field unit, WIFIS uses the Florida Image Slicer for Infrared Cosmological and Astrophysics which is a set of 3 monolithic mirror arrays housing 22 image slicers. The collimator system consists of one off-axis parabola and two lenses, while WIFIS relies on 3 different gratings to cover the entire JHK bands. The spectrograph camera uses 6 lenses of CaF2 and SFTM16, delivering the f/3 final beam onto a Hawaii II RG 2K × 2K detector array. WIFIS will be an ideal instrument to study the dynamics and chemistry of extended objects.

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)0.001
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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.663
Threshold uncertainty score0.763

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.009
GPT teacher head0.238
Teacher spread0.230 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations4
Published2010
Admission routes1
Has abstractyes

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Same venueProceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIESame topicAstronomy and Astrophysical ResearchFrench-language works237,207