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Record W1997180007 · doi:10.1117/12.2056894

MSE spectrograph optical design: a novel pupil slicing technique

2014· article· en· W1997180007 on OpenAlexaff
P. Spanò

Bibliographic record

VenueProceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2014
Typearticle
Languageen
FieldMaterials Science
TopicOptical Coatings and Gratings
Canadian institutionsHerzberg Institute of Astrophysics
Fundersnot available
KeywordsSpectrographOpticsDichroic glassSpectral resolutionCollimated lightThroughputSlicingMaterials scienceGratingBeam splitterWavelengthComputer scienceOptoelectronicsPhysicsTelecommunicationsLaserSpectral lineComputer graphics (images)

Abstract

fetched live from OpenAlex

The Maunakea Spectroscopic Explorer shall be mainly devoted to perform deep, wide-field, spectroscopic surveys at spectral resolutions from ~2000 to ~20000, at visible and near-infrared wavelengths. Simultaneous spectral coverage at low resolution is required, while at high resolution only selected windows can be covered. Moreover, very high multiplexing (3200 objects) must be obtained at low resolution. At higher resolutions a decreased number of objects (~800) can be observed. To meet such high demanding requirements, a fiber-fed multi-object spectrograph concept has been designed by pupil-slicing the collimated beam, followed by multiple dispersive and camera optics. Different resolution modes are obtained by introducing anamorphic lenslets in front of the fiber arrays. The spectrograph is able to switch between three resolution modes (2000, 6500, 20000) by removing the anamorphic lenses and exchanging gratings. Camera lenses are fixed in place to increase stability. To enhance throughput, VPH first-order gratings has been preferred over echelle gratings. Moreover, throughput is kept high over all wavelength ranges by splitting light into more arms by dichroic beamsplitters and optimizing efficiency for each channel by proper selection of glass materials, coatings, and grating parameters.

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)0.002
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: none
Teacher disagreement score0.626
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.014
GPT teacher head0.234
Teacher spread0.220 · 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
Published2014
Admission routes1
Has abstractyes

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Same venueProceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIESame topicOptical Coatings and GratingsFrench-language works237,207