CCAT-prime: observatory control software for FYST
Bibliographic record
Abstract
The Fred Young Submillimeter Telescope (FYST) is a 6-meter diameter telescope with a surface accuracy of 10 microns, operating at submillimeter to millimeter wavelengths. It will be located at 5600 meters elevation on Cerro Chajnantor in the Atacama desert of northern Chile overlooking the ALMA site. Its novel optical “crossed-Dragone” design will deliver a high-throughput, wide field-of-view telescope capable of mapping the sky very rapidly and efficiently. The telescope can host up to three instruments, with the heterodyne array “CHAI” and the direct-detection camera “Prime-Cam” as first-generation instruments. The often harsh environmental conditions at the telescope site require that FYST be operated remotely, either from the base station near San Pedro de Atacama or from the scientist’s home institutions in the US, Canada and Germany. Automated observations will therefore be the dominant observation mode. FYST’s Observatory Control System (OCS) gives instrument teams the responsibility to control observations. We believe that this model is a good fit for FYST because the observatory will operate exclusively in campaign mode. Furthermore, instrument teams have significant investments in software they want to preserve. The OCS adopts a micro-service design using off-theshelf components as far as possible to minimize development effort. We will present the OCS design and the selection of off-the-shelf components used.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.056 | 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".