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Record W2037260551 · doi:10.1086/505990

The Strehl Efficiency of Adaptive Optics Systems

2006· article· en· W2037260551 on OpenAlex
Réne Racine

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenuePublications of the Astronomical Society of the Pacific · 2006
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAdaptive optics and wavefront sensing
Canadian institutionsUniversité de MontréalUniversité du Québec à Montréal
Fundersnot available
KeywordsStrehl ratioAdaptive opticsPhysicsOpticsWavefrontDeformable mirrorCurvatureCompensation (psychology)StarsComputer scienceMathematicsAstrophysics

Abstract

fetched live from OpenAlex

Strehl ratios achieved on bright guide stars by 19 adaptive optics (AO) systems of various dimensions are examined. Both types of systems exhibit a similarly stronger attenuation of instrumental aberrations with smaller subapertures. With the same number of wave‐front sensor subapertures, curvature systems are generally found to be more efficient than Shack‐Hartmann systems at attenuating turbulence‐induced optical phase variance. Consequently, curvature systems use fainter guide stars to achieve the same performance as Shack‐Hartmann systems. The contrast is stronger for larger systems. Possible causes of these differences are discussed. Calibration errors of non–common‐path aberrations appear to be the most important. The compensation of the guide star image itself seems to be beneficial for large curvature systems. The likely performance of future very large systems are briefly discussed. A plea is made to encourage astronomical AO teams to uniformly and optimally characterize the on‐sky performance of their systems.

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.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.708
Threshold uncertainty score0.302

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.207
Teacher spread0.197 · 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