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Record W2030198327 · doi:10.1088/0004-637x/798/2/73

BANYAN. V. A SYSTEMATIC ALL-SKY SURVEY FOR NEW VERY LATE-TYPE LOW-MASS STARS AND BROWN DWARFS IN NEARBY YOUNG MOVING GROUPS

2014· article· en· W2030198327 on OpenAlexaff

Bibliographic record

VenueThe Astrophysical Journal · 2014
Typearticle
Languageen
FieldPhysics and Astronomy
TopicStellar, planetary, and galactic studies
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsBrown dwarfProper motionStarsStellar classificationExoplanetSkyGalactic plane

Abstract

fetched live from OpenAlex

We present the BANYAN All-Sky Survey (BASS) catalog, consisting of 228 new late-type (M4–L6) candidate members of nearby young moving groups (YMGs) with an expected false-positive rate of ∼13%. This sample includes 79 new candidate young brown dwarfs and 22 planetary-mass objects. These candidates were identified through the first systematic all-sky survey for late-type low-mass stars and brown dwarfs in YMGs. We cross-matched the Two Micron All Sky Survey and AllWISE catalogs outside of the galactic plane to build a sample of 98,970 potential ⩾M5 dwarfs in the solar neighborhood and calculated their proper motions with typical precisions of 5–15 mas yr −1 . We selected highly probable candidate members of several YMGs from this sample using the Bayesian Analysis for Nearby Young AssociatioNs II tool (BANYAN II). We used the most probable statistical distances inferred from BANYAN II to estimate the spectral type and mass of these candidate YMG members. We used this unique sample to show tentative signs of mass segregation in the AB Doradus moving group and the Tucana-Horologium and Columba associations. The BASS sample has already been successful in identifying several new young brown dwarfs in earlier publications, and will be of great interest in studying the initial mass function of YMGs and for the search of exoplanets by direct imaging; the input sample of potential close-by ⩾M5 dwarfs will be useful to study the kinematics of low-mass stars and brown dwarfs and search for new proper motion pairs.

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)0.001
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.020
Threshold uncertainty score0.666

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.015
GPT teacher head0.226
Teacher spread0.212 · 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 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

Citations110
Published2014
Admission routes1
Has abstractyes

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