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Record W2094234509 · doi:10.1117/12.860849

Tunable filter imager for JWST: etalon opto-mechanical design and test results

2010· article· en· W2094234509 on OpenAlexaff
D. Touahri, Peter Cameron, C. J. Evans, C. S. Haley, Zeljko Osman, Alan Scott, Neil Rowlands

Bibliographic record

VenueProceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2010
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAdaptive optics and wavefront sensing
Canadian institutionsCOM DEV International
Fundersnot available
KeywordsFabry–Pérot interferometerJames Webb Space TelescopeOptical filterFilter (signal processing)Materials scienceOpticsComputer scienceOptoelectronicsPhysicsComputer visionWavelength

Abstract

fetched live from OpenAlex

The Tunable Filter Imager of the James Webb Space Telescope will be based on blocking filters and a tunable Fabry- Perot etalon with an average resolution of about 100. It will operate in two wavelength bands from 1.6 μm to 2.5 μm and from 3.1 μm to 4.9 μm at a cryogenic temperature of about 35K. It will respectively be used to study the First Light and re-ionization of the universe by surveying Lyman-alpha sources and to provide an in-depth study of proto-planetary systems as well as giant planets of nearby stars. The Tunable Filter Imager (TFI) is designed to image a sky field of view of 2.2' by 2.2' (magnified to 4.6 deg. x 4.6 deg. at the etalon). Its tunable etalon has an aperture of 56 mm. It operates at low orders 1 and 3 for the two wavelength bands which reduces the number of blocking filters to a number of eight. The etalon gap tuning between 2.5 μm and 5.5 μm is provided by piezoelectric actuators and will be servo controlled by using capacitive displacement sensors. In this paper, we present the etalon's opto-mechanical design that allows us to achieve the stringent requirements in terms of resolution over a wide infrared wavelength band, and operation at low gap at cryogenic temperature. Cryogenic test results will be shared as well.

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)0.001
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: Empirical
Teacher disagreement score0.455
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.012
GPT teacher head0.227
Teacher spread0.215 · 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
Published2010
Admission routes1
Has abstractyes

Explore more

Same venueProceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIESame topicAdaptive optics and wavefront sensingFrench-language works237,207