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Record W2493253300 · doi:10.1117/12.2233172

Status and performance of the Gemini Planet Imager adaptive optics system

2016· article· en· W2493253300 on OpenAlexaff
Vanessa P. Bailey, Lisa Poyneer, Bruce Macintosh, Dmitry Savransky, Jason Wang, Robert J. De Rosa, Katherine B. Follette, S. Mark Ammons, T. L. Hayward, Patrick Ingraham, Jérôme Maîre, David W. Palmer, Marshall D. Perrin, Abhijith Rajan, Fredrik T. Rantakyrö, Sandrine Thomas, Jean‐Pierre Véran

Bibliographic record

VenueProceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2016
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAdaptive optics and wavefront sensing
Canadian institutionsHerzberg Institute of AstrophysicsUniversity of Toronto
Fundersnot available
KeywordsExoplanetAdaptive opticsWavefrontContrast (vision)OpticsPhysicsPlanetComputer scienceRemote sensingSoftwareAstronomyGeology

Abstract

fetched live from OpenAlex

The Gemini Planet Imager is a high-contrast near-infrared instrument specifically designed to image exoplanets and circumstellar disks over a narrow field of view. We use science data and AO telemetry taken during the first 1.5 yr of the GPI Exoplanet Survey to quantify the performance of the AO system. In a typical 60 sec H-band exposure, GPI achieves a 5σ raw contrast of 10−4 at 0.4"; typical final 5σ contrasts for full 1 hr sequences are more than 10 times better than raw contrasts. We find that contrast is limited by bandwidth wavefront error over much of the PSF. Preliminary exploratory factor analysis can explain 60{70% of the variance in raw contrasts with combinations of seeing and wavefront error metrics. We also examine the effect of higher loop gains on contrast by comparing wavefront error maps reconstructed from AO telemetry to concurrent IFS images. These results point to several ways that GPI performance could be improved in software or hardware.

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)0.006
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.030

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.003
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.002

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 head0.009
GPT teacher head0.203
Teacher spread0.194 · 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 designNot applicable
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

Citations28
Published2016
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