MétaCan
Menu
Back to cohort
Record W4399670512 · doi:10.1117/12.3018227

Evolving the METIS soft real-time control system out of the simulation environment

2024· article· en· W4399670512 on OpenAlexaboutno aff
Horst Steuer, M. Feldt, Thomas Bertram, Carlos Correia, Andreas Obereder, Hugo Coppejans, Martin Kulas, Silvia Scheithauer, M. Vázquez, Daniel J. Mortimer, Gilles Orban de Xivry, Olivier Absil

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicReal-time simulation and control systems
Canadian institutionsnot available
FundersMax-Planck-Institut für Astronomie
KeywordsMetisComputer scienceReal-time Control SystemControl (management)Artificial intelligenceWorld Wide Web

Abstract

fetched live from OpenAlex

METIS, the Mid-Infrared ELT Imager and Spectrograph is a first-generation ELT instrument scheduled to see first light in 2029. Its two main science modules are supported by an adaptive optics system featuring a pyramid sensor with 90x90 sub-apertures working in H- and K-band. The wavefront control concept for METIS’ single-conjugate adaptive optics relies on a synthetic calibration that uses a model of the telescope and instrument to generate the interaction and control matrices, as well as the final projection on a modal command vector. This concept is enforced owing to the absence of a calibration source in front of the ELT’s main deformable mirror. The core of the synthetic calibration functionality is the Command Matrix Optimiser module, which consists of several components providing models for various parts and aspects of the instrument, as well as the entire reconstructor. Many are present in the simulation environment used during the design phases, but need to be re-written and/or adapted for real-life use. In this paper, we present the design of the full command matrix optimisation module, the status of these efforts and the overall final concept of METIS’ soft real-time system.

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.958
Threshold uncertainty score0.461

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 head0.006
GPT teacher head0.198
Teacher spread0.192 · 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

Citations2
Published2024
Admission routes1
Has abstractyes

Explore more

Same topicReal-time simulation and control systemsFrench-language works237,207