MétaCan
Menu
Back to cohort
Record W4293525509 · doi:10.1117/12.2630645

The multi-object adaptive optics system for the Gemini infra-red multi-object spectrograph

2022· article· en· W4293525509 on OpenAlexaff
S. C. Chapman, Uriel Conod, Paolo Turri, Kate Jackson, Olivier Lardière, Suresh Sivanandam, David R. Andersen, Carlos Correia, Masen Lamb, Colin A. Ross, Gaetano Sivo, Jean‐Pierre Véran

Bibliographic record

VenueAdaptive Optics Systems VIII · 2022
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAdaptive optics and wavefront sensing
Canadian institutionsUniversity of British ColumbiaDalhousie UniversityHerzberg Institute of Astrophysics
Fundersnot available
KeywordsSpectrographAdaptive opticsIntegral field spectrographPhysicsComputer scienceGalactic astronomyOpticsGalaxyAstronomyMilky Way

Abstract

fetched live from OpenAlex

The Gemini Infra-Red Multi-Object Spectrograph (GIRMOS) is a four-arm, Multi-Object Adaptive Optics (MOAO) IFU spectrograph being built for Gemini (commissioning in 2024). GIRMOS is being planned to interface with the new Gemini-North Adaptive Optics (GNAO) system, and is base lined with a requirement of 50% EE within a 0.100 spaxel at H-band. We present a design and forecast the error budget and performance of GIRMOS-MOAO working behind GNAO. The MOAO system will patrol the 20 field of regard of GNAO, utilizing closed loop GLAO or MCAO for lower order correction. GIRMOS MOAA will perform tomographic reconstruction of the turbulence using the GNAO WFS, and utilize order 16x16 actuator DMs operating in open loop to perform an additional correction from the Pseudo Open Loop (POL) slopes, achieving close to diffraction limited performance from the combined GNAO+MOAO correction. This high performance AO spectrograph will have the broadest impact in the study of the formation and evolution of galaxies, but will also have broad reach in fields such as star and planet formation within our Milky Way and supermassive black holes in nearby galaxies.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.966
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0050.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.001
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 head0.028
GPT teacher head0.253
Teacher spread0.225 · 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.

Study designSimulation or modeling
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

Citations2
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueAdaptive Optics Systems VIIISame topicAdaptive optics and wavefront sensingFrench-language works237,207