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Record W2084437067 · doi:10.1117/12.552529

Innovations within the Altair real-time wavefront reconstructor

2004· article· en· W2084437067 on OpenAlexaff
Leslie Saddlemyer, Glen Herriot, Jean-Pierre Vrran, Malcolm J. Smith, Jennifer Dunn

Bibliographic record

VenueProceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2004
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAdaptive optics and wavefront sensing
Canadian institutionsHerzberg Institute of Astrophysics
Fundersnot available
KeywordsWavefrontCentroidAdaptive opticsComputer scienceDeformable mirrorFocus (optics)Tilt (camera)Background subtractionReal-time computingBandwidth (computing)SimulationComputer visionArtificial intelligenceActuatorOpticsPhysicsEngineeringTelecommunications

Abstract

fetched live from OpenAlex

The Gemini North adaptive optics system Altair utilises five cooperative CPU's to perform all the associated real-time tasks. One, the reconstructor (RTC), manages all of the highest speed hard real-time duties. As well as the core, computationally intensive, wavefront reconstruction, this processor implements a number of algorithms providing control system support services. These include: the quad-cell centroid gain estimation, determination and subtraction of invisible modes on the deformable mirror, and the blending of tip, tilt and focus from the on instrument wavefront sensors (which exist on all facility Gemini instruments). These associated support tasks are critically important to ensure that the system always runs with an optimal bandwidth and produce stable images with no artefacts such as a waffle pattern or residual non-common path errors. We present the original algorithm that we have developed for the centroid gain estimate and discuss how it is efficiently and conveniently implemented on the hard real-time processor.

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 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 score0.528
Threshold uncertainty score0.919

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 studies0.0000.000
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 head0.010
GPT teacher head0.220
Teacher spread0.210 · 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

Citations0
Published2004
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