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Record W4285982431 · doi:10.1117/12.2630620

Mechanical design and integration of the Gemini High-Resolution Optical Spectrograph (GHOST) lens barrels assemblies.

2022· article· en· W4285982431 on OpenAlexaffabout
John Pazder, André Anthony, Alan W. McConnachie, Greg Burley

Bibliographic record

VenueGround-based and Airborne Instrumentation for Astronomy IX · 2022
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAstronomy and Astrophysical Research
Canadian institutionsHerzberg Institute of Astrophysics
Fundersnot available
KeywordsSpectrographLens (geology)OpticsRigidity (electromagnetism)TelescopeTilt (camera)Optical telescopeMechanical designAdaptive opticsPhysicsEngineeringMechanical engineeringAstronomy

Abstract

fetched live from OpenAlex

GHOST instrument is being developed for the Gemini telescope and is a collaboration between AAO, the Herzberg Astronomy and Astrophysics (HAA) in Canada and the Australian National University (ANU). The instrument is a fiber fed spectrograph with R<50,000 in two-object mode and R<75,000 in single object mode. This paper presents mechanical design and optical alignment of the lens barrels used for the two cameras in the instrument. A divide and conquer approach was used. Camera lenses were aligned to their respective individual cells for the tilt and axial spacing, and then bonded to flexures for stability. Centering of the lens to each other was done as the individual cells were stacked into barrels. Alignment in tilt and axial position was performed on Laser Alignment and Assembly Station (LAS) manufactured by Opto-Alignment Technology, Inc. Design of the individual optical cells provides low-stress restraint and at the same time ensures high rigidity and long-term mechanical stability of the mount.

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: none
Teacher disagreement score0.714
Threshold uncertainty score0.788

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 studies0.0010.000
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 head0.019
GPT teacher head0.260
Teacher spread0.240 · 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

Citations4
Published2022
Admission routes2
Has abstractyes

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