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Record W2826141582 · doi:10.1117/12.2309324

Image slicer module for Wide Field Optical Spectrograph (WFOS)

2018· article· en· W2826141582 on OpenAlexfundno aff
Toshihiro Tsuzuki, Shinobu Ozaki, Satoshi Miyazaki, Jason Fucik

Bibliographic record

VenueGround-based and Airborne Instrumentation for Astronomy VII · 2018
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAstronomy and Astrophysical Research
Canadian institutionsnot available
FundersOntario Ministry of Research and InnovationBritish Columbia Knowledge Development FundNatural Sciences and Engineering Research Council of CanadaNational Astronomical Observatory of JapanAssociation of Canadian Universities for Research in AstronomyNational Institutes of Natural SciencesCalifornia Institute of TechnologyGordon and Betty Moore FoundationNational Science Foundation
KeywordsSpectrographGratingOpticsIntegral field spectrographTelescopeField of viewSpectral resolutionComputer sciencePhysicsComputer graphics (images)Spectral line

Abstract

fetched live from OpenAlex

Wide-Field Optical Spectrograph (WFOS) is an optical multi-object spectrograph and one of the first-light instruments of Thirty Meter Telescope (TMT). The WFOS development team has studied three new instrument concepts. One is a fiber-based spectrograph, and other one is a spectrograph using image slicers (Slicer-WFOS). The last one is the simple multi-slit spectrograph. Japanese WFOS team has conducted conceptual studies on Slicer-WFOS in collaboration with California Institute of Technology. Slicer-WFOS has only one VPH grating for each red and blue arm. The gratings offer R~1,500 for a simple 0.″75-width slit. The image slicer divides an object image into three slices and the higher spectral resolution of R~4,500 can be achieved using the same grating. In this proceeding paper, we report our design studies on the slicer module.

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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.915
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
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 head0.011
GPT teacher head0.288
Teacher spread0.277 · 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 designOther design
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
Published2018
Admission routes1
Has abstractyes

Explore more

Same venueGround-based and Airborne Instrumentation for Astronomy VIISame topicAstronomy and Astrophysical ResearchFrench-language works237,207