The METIS instrument control system
Bibliographic record
Abstract
METIS, the Mid-infrared ELT Imager and Spectrograph, will be one of the first-generation ELT instruments.It has an instrument control system (ICS) that allows the instrument to operate in various observing modes, using a multitude of mostly cryogenic mechanisms such as filter wheels, linear stages (e.g. to move mirrors and masks), a derotator, piezo mechanisms for high-speed or high-accuracy displacements (e.g. for pupil stabilization, for adaptive optics field selection and modulation, ...), a chopper, and so on.Thermal and vacuum control of the cryostat on the other hand is handled by a dedicated PLC-based system and is not described in this paper.The ICS is built using the ESO ELT instrument framework, which provides the basic building blocks to control the mechanisms, along with the interface to the telescope and its services, from the low-level pointing and tracking system, the real-time Single Conjugate Adaptive Optics (SCAO) system, to the high-level sequencer-based observing system.In this paper we provide an overview of the ICS electronics, the low-level software running on a Beckhoff PLC, and finally the high-level software running on a Linux workstation.As a detailed description of the entire system is out of scope of the paper, we focus instead on the general design, implementation and testing principles.We show how a fast real-time network (EtherCAT) and off-the-shelf industrial I/O, together with the services provided by the ELT instrument framework, can meet the requirements of ELT instruments, and how they can offer an elegant solution to technically demanding problems such as the high-speed synchronization between a chopper and a detector controller.Finally, we demonstrate how a modular electronics design, a flexible software architecture, and a strong focus on simulation can alleviate some of the organizational challenges of building, integrating and testing an ICS of a complex instrument, which subsystems are developed by institutes in different locations.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.027 | 0.027 |
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".