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Record W2883650377 · doi:10.3847/1538-3881/aad599

WEIRD: Wide-orbit Exoplanet Search with InfraRed Direct Imaging

2018· article· en· W2883650377 on OpenAlexaff
Frédérique Baron, Étienne Artigau, Julien Rameau, David Lafreniére, Jonathan Gagné, Lison Malo, Loïc Albert, Marie-Ève Naud, René Doyon, M. Janson, P. Delorme, Charles Beichman

Bibliographic record

VenueThe Astronomical Journal · 2018
Typearticle
Languageen
FieldPhysics and Astronomy
TopicStellar, planetary, and galactic studies
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsExoplanetPhysicsStarsAstronomyInfraredOrbit (dynamics)Direct imagingAstrophysicsOptics

Abstract

fetched live from OpenAlex

Abstract We report results from the Wide-orbit Exoplanet search with InfraRed Direct imaging, or WEIRD, a survey designed to search for Jupiter-like companions on very wide orbits (1000–5000 au) around young stars (<120 Myr) that are known members of moving groups in the solar neighborhood (<70 pc). Companions that share the same age, distance, and metallicity as their host while being on large enough orbits to be studied as “isolated” objects make prime targets for spectroscopic observations, and they are valuable benchmark objects for exoplanet atmosphere models. The search strategy is based on deep imaging in multiple bands across the near-infrared domain. For all 177 objects of our sample, , J , [3.6], and [4.5] images were obtained with CFHT/MegaCam, GEMINI/GMOS, CFHT/WIRCam, GEMINI/Flamingos-2, and Spitzer /IRAC. Using this set of four images per target, we searched for sources with red and [3.6]–[4.5] colors, typically reaching good completeness down to 2 companions, while going down to 1 for some targets, at separations of 1000–5000 au. The search yielded four candidate companions with the expected colors, but they were all rejected through follow-up proper motion observations. Our results constrain the occurrence of 1–13 planetary-mass companions on orbits with a semimajor axis between 1000 and 5000 au at less than 0.03, with a 95% confidence level.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.123
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.009
GPT teacher head0.223
Teacher spread0.213 · 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 designObservational
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

Citations18
Published2018
Admission routes1
Has abstractyes

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