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Record W3087625851 · doi:10.5281/zenodo.1344651

Big Planets, Little Stars: Directly Imaged Companions To Young M-Stars

2018· article· en· W3087625851 on OpenAlexaff
Henry Ngo, Dimitri Mawet, Garreth Ruane, Jerry W. Xuan, Brendan P. Bowler, Therese Cook, Zoë Zawol

Bibliographic record

VenueFigshare · 2018
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAstronomy and Astrophysical Research
Canadian institutionsHerzberg Institute of Astrophysics
Fundersnot available
KeywordsStarsPlanetAstronomyAstrobiologyPhysicsHabitability of orange dwarf systemsK-type main-sequence starAstrophysicsT Tauri star

Abstract

fetched live from OpenAlex

Abstract: We present new results from our search for directly imaged planets and brown dwarfs around 200 young (10-150 Myr) M-stars in the L-band (3.8 microns). The new vector vortex coronagraph on Keck/NIRC2 allows us to find companions as close as 80 milliarcsecond, which enables our survey to be sensitive to young planets with masses between 1-10 Jupiter masses and with projected separations between 1-10 au. The initial survey is complete and follow-up observations of 40 candidate companions are planned for 2018. While transit and radial velocity detection techniques have probed giant planet populations at close separations (within a few au), this survey will determine the occurrence rate of giant planets around small stars at larger separations. We will also be sensitive to brown dwarf companions, allowing us to identify new multiple systems of low mass stars. Our results demonstrates the potential to explore the planet occurrence rate and multiplicity of nearby low-mass objects at young ages.

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.001

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 head0.033
GPT teacher head0.290
Teacher spread0.256 · 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 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

Citations0
Published2018
Admission routes1
Has abstractyes

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