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Record W1994575848 · doi:10.1117/12.2055196

First performance of the GeMS+GMOS system

2014· article· en· W1994575848 on OpenAlexaff
Pascale Hibon, Benoît Neichel, Vincent Garrel, Benjamin Prout, François Rigaut, Alice de Koning, Gaetano Sivo, G. Gimeno, E. R. Carrasco, Cláudia Winge, Peter Pessev, Andrew Serio, Gustavo Arriagada

Bibliographic record

VenueProceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2014
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAdaptive optics and wavefront sensing
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsUpgradeObservatorySpectrographConstellationMode (computer interface)Computer sciencePhysicsOpticsRemote sensingAstronomyOperating systemGeologySpectral line

Abstract

fetched live from OpenAlex

During the 2012 commissioning of the Gemini MCAO System (GeMS) in Gemini South Observatory, we briefly explored the performance improvement brought by pairing GeMS with the Gemini Multi-Object Spectrograph (GMOS), compared to GMOS in natural seeing mode. GMOS is an instrument sensitive in the visible band with imaging and spectroscopic capabilities, hence pushing MCAO toward the visible, a mode for which it was not specifically designed. We report in this paper the first results obtained with the GeMS +GMOS pair. Several globular clusters were observed in imaging mode only. We have derived performance in term of FWHM and determined the improvement against natural seeing. We also obtain photometric, relative and absolute astrometric precision for the AO enhanced images. We also studied the influence of the NGS constellation on the photometric performance. Finally, we also looked at the expected performance of the GeMS+GMOS system once the CCD upgrade, scheduled during 2014, will occur.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.001

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 head0.008
GPT teacher head0.197
Teacher spread0.189 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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

Citations3
Published2014
Admission routes1
Has abstractyes

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Same venueProceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIESame topicAdaptive optics and wavefront sensingFrench-language works237,207