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Record W1997220725 · doi:10.2971/jeos.2010.10055

Synthetic modeling of astronomical closed loop adaptive optics

2010· article· en· W1997220725 on OpenAlexfundno aff
Laurent Jolıssaınt

Bibliographic record

VenueJournal of the European Optical Society Rapid Publications · 2010
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAdaptive optics and wavefront sensing
Canadian institutionsnot available
FundersNational Research Council CanadaUniversiteit Leiden
KeywordsAdaptive opticsWavefrontGuide starDeformable mirrorOpticsWavefront sensorTransfer functionAliasingPhysicsPoint spread functionSpatial filterComputer scienceFilter (signal processing)Computer visionEngineering

Abstract

fetched live from OpenAlex

We present an analytical model of a single natural guide star astronomical adaptive optics system, in closed loop mode. The model is used to simulate the long exposure system point spread function, using the spatial frequency (or Fourier) approach, and complements an initial open loop model. Applications range from system design, science case analysis and AO data reduction. All the classical phase errors have been included: deformable mirror fitting error, wavefront sensor spatial aliasing, wavefront sensor noise, and the correlated anisoplanatic and servo-lag error. The model includes the deformable mirror spatial transfer function, and the actuator array geometry can be different from the wavefront sensor lenslet array geometry. We also include the dispersion between the sensing and the correction wavelengths. Illustrative examples are given at the end of the paper.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.006
Threshold uncertainty score0.012

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 studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.020
GPT teacher head0.232
Teacher spread0.212 · 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 designSimulation or modeling
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

Citations62
Published2010
Admission routes1
Has abstractyes

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