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Record W3108239865

The Critical, Strategic Importance of Adaptive Optics-Assisted Ground-Based Telescopes for the Success of Future NASA Exoplanet Direct Imaging Missions

2019· article· en· W3108239865 on OpenAlexaff
Thayne Currie, Ruslan Belikov, Olivier Guyon, N. Jeremy Kasdin, Christian Marois, Mark S. Marley, Kerri Cahoy, Dimitri Mawet, Michael W. McElwain, Eduardo Bendek, Marc J. Kuchner, Michael R. Meyer

Bibliographic record

VenueCaltechAUTHORS (California Institute of Technology) · 2019
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAdaptive optics and wavefront sensing
Canadian institutionsHerzberg Institute of Astrophysics
Fundersnot available
KeywordsExoplanetPlanetDirect imagingAdaptive opticsAstrobiologyRemote sensingPhysicsSolar SystemAstronomyTerrestrial planetSystems engineeringAerospace engineeringOpticsEngineeringGeography
DOInot available

Abstract

fetched live from OpenAlex

Ground-based telescopes coupled with adaptive optics (AO) have been playing a leading role in exoplanet direct imaging science and technological development for the past two decades and will continue to have an indispensable role for the next decade and beyond. Over the next decade, extreme AO systems on 8-10m telescopes will 1) mitigate risk for WFIRST-CGI by identifying numerous planets the mission can spectrally characterize, 2) validate performance requirements and motivate improvements to atmosphere models needed to unambiguously characterize solar system-analogues from space, and 3) mature novel technological innovations useful for space. Extremely Large Telescopes can deliver the first thermal infrared (10 μm) images of rocky planets around Sun-like stars and identify biomarkers. These data provide a future NASA direct imaging flagship mission (i.e. HabEx, LUVOIR) with numerous exo-Earth candidates and critical ancillary information to help clarify whether these planets are habitable.

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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.766
Threshold uncertainty score0.752

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.268
Teacher spread0.249 · 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 designTheoretical or conceptual
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
Published2019
Admission routes1
Has abstractyes

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