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Record W3048252336 · doi:10.1109/aim43001.2020.9158936

A Crossover Network based Control Concept for the Tip-Tilt Rejection in the Mid-Infrared ELT Imager and Spectrograph (METIS)

2020· article· en· W3048252336 on OpenAlexaboutno aff
Philip L. Neureuther, Thomas Bertram, Oliver Sawodny

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAdaptive optics and wavefront sensing
Canadian institutionsnot available
Fundersnot available
KeywordsMetisWavefrontSpectrographTilt (camera)Wavefront sensorAdaptive opticsOpticsPhysicsController (irrigation)Computer scienceEngineeringAstronomy

Abstract

fetched live from OpenAlex

The telescope instrument Mid-infrared ELT Imager and Spectrograph (METIS) is currently under development and will enable high-resolution spectroscopic and coronagraphic imaging. To obtain diffraction-limited measurements despite wavefront disturbances, METIS uses a single conjugate adaptive optics (SCAO) system comprising the active mirrors M4/M5, a pyramid wavefront sensor, and a controller. Because tip-tilt wavefront disturbances are dominant, the main task of the METIS-SCAO system is their rejection. Since M4 and M5 can redundantly correct tip-tilt errors and M4 features much faster dynamics and smaller actuator strokes than M5, the METIS-SCAO system is a dual-stage system. In order to achieve a proper tip-tilt correction for METIS while splitting this correction efficiently between the active mirrors, we present a new crossover network (CN) based control concept. This concept assigns low-frequency high amplitude tip-tilt error components to M5 and high-frequency small amplitude components to M4 via a specific type of CNs. Subsequently, two independent single-input single-output controller command the active mirrors, such that the assigned error components are corrected. Simulations show that the presented control concept rejects tip-tilt wavefront errors well for medium observation conditions.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.953
Threshold uncertainty score0.320

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 science0.0000.000
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 head0.013
GPT teacher head0.234
Teacher spread0.221 · 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 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

Citations0
Published2020
Admission routes1
Has abstractyes

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