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Record W2052393433

Control System Performance of a Woofer-Tweeter Adaptive Optics System

2006· article· en· W2052393433 on OpenAlexaff
Peter J. Hampton, Colin Bradley, P. Agathoklis, Rodolphe Conan

Bibliographic record

Venueamos · 2006
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAdaptive optics and wavefront sensing
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsAdaptive opticsDeformable mirrorActuatorPhysicsOpticsWavefrontTelescopeDistortion (music)Computer scienceControl theory (sociology)Artificial intelligenceOptoelectronics
DOInot available

Abstract

fetched live from OpenAlex

A simple adaptive optics system for astronomy uses a single wave front sensor (WFS) and a single deformable mirror (DM) to correct for the distortions imposed on light by the atmosphere and the static aberrations of the telescope optics [1]. In the next generation telescopes, both the actuator density and maximum actuator stroke requirements increase significantly due to the enormity of these very large telescopes. Current technology is cost prohibitive to design a single mirror that satisfies both of these requirements. Fortunately, the large stroke required is for the compensation of low spatial frequency distortion [2]. This allows the system to be designed with two DMs; (i) a high stroke, low actuator density DM named the Woofer and (ii) a low stroke, high actuator density DM named the Tweeter. The Adaptive Optics Laboratory at the University of Victoria has recently produced a test bench for this Woofer-Tweeter system. This project is part of the development of the Thirty Meter Telescope (TMT) that will be built in the next decade. Initial simulated and experimental results have shown that the developed controller can appropriately split the correction between the mirrors and acts similarly to the single DM case shown in [3]. This paper focuses primarily on discrete control and the Z-domain [4]. The Woofer corrects for the low-spatial-low-temporal frequency disturbances and the Tweeter corrects for the remaining disturbance. It has been assumed that the Woofer can respond slower than the tweeter. The Woofer’s impulse response is modeled as an exponential decay, e-kT/τ. A one dimensional representation of the controller approach is shown in Figure 1. The Woofer slowly approaches the steady state of the input signal. During this time, the Tweeter compensates for the residual error. The combined response of the two DMs is then equal to how the single DM case would respond to the input. A simple layout of the system components is shown in Figure 2.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
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 score0.005
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0050.001

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 head0.006
GPT teacher head0.184
Teacher spread0.178 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations1
Published2006
Admission routes1
Has abstractyes

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