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Record W4401934405 · doi:10.1117/12.3020360

GPI 2.0: pre-integrated pyramid wavefront sensor results

2024· article· en· W4401934405 on OpenAlexaff
Saavidra Perera, Jérôme Maîre, Clarissa R. Do Ó, Jayke S. Nguyen, Vincent Chambouleyron, Quinn Konopacky, Jeffrey Chilcote, Brian Sands, Randall Hamper, Matthew Engstrom, Joeleff Fitzsimmons, Dan Kerley, Bruce Macintosh, Christian Marois, Fredrik T. Rantakyrö, Dmitry Savransky, Jean‐Pierre Véran, Guido Agapito, S. Mark Ammons, Marco Bonaglia, Marc-André Boucher, Joel Burke, Daren Dillon, Jennifer Dunn, Simone Esposito, Guillaume J. Filion, Oyku Galvan, Jean-Thomas Landry, Olivier Lardière, Duan Li, Alexander Madurowicz, Teo Močnik, Dillon Peng, Lisa Poyneer, Carlos Quiroz, Garima Singh, Eckhart Spalding

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldPhysics and Astronomy
TopicStellar, planetary, and galactic studies
Canadian institutionsNational Research Council Canada
Fundersnot available
KeywordsWavefront sensorAdaptive opticsWavefrontUpgradeTelescopeExoplanetPhysicsComputer scienceOpticsImage qualityRemote sensingPlanetArtificial intelligenceAstronomyGeologyImage (mathematics)

Abstract

fetched live from OpenAlex

The Gemini Planet Imager (GPI) is a high-contrast imaging instrument designed to directly detect and characterise young, Jupiter-mass exoplanets. After six years of operation at the Gemini South Telescope in Chile, the instrument is being upgraded and moved to the Gemini North Telescope in Hawaii as GPI 2.0. Several improvements have been made to the adaptive optics (AO) system as part of this upgrade. This includes replacing the current Shack-Hartmann wavefront sensor with a pyramid wavefront sensor (PWFS) and a custom EMCCD. These changes will increase GPI’s sky coverage by accessing fainter targets, improving corrections on fainter stars and allowing faster and ultra-low latency operations on brighter targets. The PWFS subsystem was independently built and tested to verify its performance before being integrated into the GPI 2.0 instrument. This paper will present the pre-integration performance test results, including pupil image quality, throughput and linearity without modulation.

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)0.003
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 score0.012
Threshold uncertainty score0.040

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0120.004

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.

Opus teacher head0.011
GPT teacher head0.239
Teacher spread0.228 · 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

Citations0
Published2024
Admission routes1
Has abstractyes

Explore more

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