Much-needed improvements to the 4m CFHT mirror coating facility
Bibliographic record
Abstract
The 4m filament-based coating chamber at the Canada-France-Hawaii Telescope (CFHT) has been in service since before first light in 1979. Even then, the chamber produced thinner coatings than were desired - a problem that continued to plague the facility for the next forty years. Over that time the stripping and cleaning procedure was improved, as was the procedure for maintaining the coating while in service, but efforts to improve coating thickness had so far fallen short. In 2019, after forty years of continued use, cracking in the filament electrodes necessitated modifications to the chamber. These were completed in 2020 along with an overhaul of the chamber's vacuum system and thickness monitor. When the test-fire of the chamber again produced a thin coating, we took the opportunity to take a fresh look at possible underlying causes so that we could attempt to address them prior to the next mirror coating. The work was completed just in advance of the 2020 coating of the CFHT M1. Both the test-fire and the M1 coating exceeded 1000Å thickness, a first for CFHT. In this paper, we share details of the renovations to the chamber, the findings of the thin coating investigation, the further changes we made to the chamber to address them, and the resulting coatings.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.013 | 0.004 |
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".