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Record W4285585979 · doi:10.1117/12.2629691

Much-needed improvements to the 4m CFHT mirror coating facility

2022· article· en· W4285585979 on OpenAlexaboutno aff
Benedict Thomas, Marc Baril, Gregory Barrick, Greg Green, Grant Matsushige

Bibliographic record

VenueAdvances in Optical and Mechanical Technologies for Telescopes and Instrumentation V · 2022
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAdaptive optics and wavefront sensing
Canadian institutionsnot available
Fundersnot available
KeywordsCoatingMaterials scienceTelescopeComposite materialOpticsNuclear engineeringPhysicsEngineering

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.806
Threshold uncertainty score0.382

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.014
GPT teacher head0.275
Teacher spread0.261 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designOther design
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2022
Admission routes1
Has abstractyes

Explore more

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