MAORY RTC, a status update
Bibliographic record
Abstract
The Multi Conjugate Adaptive Optics RelaY (MAORY) for the ESO Extremely Large Telescope (ELT) is an Adaptive Optics module offering Multi-Conjugate (MCAO) and Single-Conjugate (SCAO) compensation modes. In MCAO, it relies on the use of a constellation of Laser Guide Stars (LGS) and up to three Natural Guide Stars (NGS) for atmospheric turbulence sensing, and multiple deformable mirrors for correction, providing uniform, high Strehl and high sky coverage. MAORY will be installed at the Nasmyth focus of the E-ELT and will feed the MICADO first-light diffraction limited imager and a future second instrument. MAORY is being built by a Consortium composed by INAF in Italy, IPAG in France and the School of Physics at the National University of Ireland Galway. In this paper we report about the status of the design of the MAORY Real Time Computer, which is the component in charge of implementing the main AO control loops, as well as of auxiliary computations to keep the loops operating optimally, and of telemetry data collection for postprocessing, monitoring, testing and troubleshooting. We will start by discussing the evolution of requirements towards MAORY RTC, with an emphasis on the main driving ones. Then, we will describe how the analysis of requirements has led to the derivation of the main design parameters. Finally, we will illustrate possible RTC designs satisfying user requirements, while also complying with standards set forth by ESO.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.094 | 0.108 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".