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Record W2082212707 · doi:10.1117/12.918708

High fidelity optical modeling for the TMT

2011· article· en· W2082212707 on OpenAlexfundno aff
Carl Nissly, Byoung-Joon Seo, Mitchell Troy, George Z. Angeli, Myung‐Haing Cho, Brent L. Ellerbroek, Piotr Piatrou, Lewis C. Roberts, J. C. Shelton, Lianqi Wang

Bibliographic record

VenueProceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2011
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAdaptive optics and wavefront sensing
Canadian institutionsnot available
FundersOntario Ministry of Research and InnovationBritish Columbia Knowledge Development FundNatural Sciences and Engineering Research Council of CanadaJet Propulsion LaboratoryAssociation of Canadian Universities for Research in AstronomyNational Aeronautics and Space AdministrationCalifornia Institute of TechnologyGordon and Betty Moore FoundationNational Science Foundation
KeywordsAdaptive opticsWavefrontComputer scienceDeformable mirrorPrimary mirrorOpticsWavefront sensorProcess (computing)TelescopeFidelityActive opticsPhysics

Abstract

fetched live from OpenAlex

The Thirty Meter Telescope (TMT) is a Ritchey-Chritien optical telescope with a 30-meter diameter primary mirror made up of 492 hexagonal segments. Such a large and complex optical system requires detailed modeling of the optical performance during the design phase. An optical modeling computational framework has been developed to support activities related to wavefront & image performance prediction. The model includes effects related to mirror shape sensing & control, mirror alignment & phasing, M1 segment control, low order wavefront correction, adaptive optics simulation for high order wavefront correction, and high contrast imaging. Here we give an overview of this optical simulation framework, the modeling tools and algorithms that are used, and a set of sample analyses. These tools have been used in many aspects of the system design process from mirror specification to instrument & sensor design to algorithm development and beyond.

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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.889
Threshold uncertainty score0.943

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.022
GPT teacher head0.232
Teacher spread0.209 · 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 designTheoretical or conceptual
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
Published2011
Admission routes1
Has abstractyes

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