Thirty Meter Telescope: observatory software requirements, architecture, and preliminary implementation strategies
Bibliographic record
Abstract
The Thirty Meter Telescope (TMT) will be a ground-based, 30-m optical-IR alt-az telescope with a highly segmented primary mirror located in a remote location. Efficient science operations require the asynchronous coordination of many different sub-systems including telescope mount, three independent active optics sub-systems, adaptive optics, laser guide stars, and user-configured science instrument. An important high-level requirement is target acquisition and observatory system configuration must be completed in less than 5 minutes (or 10 minutes if moving to a new instrument). To meet this coordination challenge and target acquisition time requirement, a distributed software architecture is envisioned consisting of software components linked by a service-based software communications backbone. A master sequencer coordinates the activities of mid-layer sequencers for the telescope, adaptive optics, and selected instrument. In turn, these mid-layer sequencers coordinate the activities of groups of sub-systems. In this paper, TMT observatory requirements are presented in more detail, followed by a description of the design reference software architecture and a discussion of preliminary implementation strategies.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".