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Record W2074648364 · doi:10.1088/0004-637x/745/2/119

CLOSE COMPANIONS TO YOUNG STARS. I. A LARGE SPECTROSCOPIC SURVEY IN CHAMAELEON I AND TAURUS-AURIGA

2012· article· en· W2074648364 on OpenAlexaff
Duy Cuong Nguyen, A. Brandeker, M. H. van Kerkwijk, Ray Jayawardhana

Bibliographic record

VenueThe Astrophysical Journal · 2012
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAstrophysics and Star Formation Studies
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsPhysicsAstrophysicsStarsT Tauri starRadial velocityPlanetAstronomyAccretion (finance)Telescope

Abstract

fetched live from OpenAlex

We present the results of a multiplicity survey of 212 T Tauri stars in the Chamaeleon I and Taurus-Auriga star-forming regions, based on high-resolution spectra from the Magellan Clay 6.5 m telescope. From these data, we achieved a typical radial velocity (RV) precision of ∼80 m s −1 with slower rotators yielding better precision, in general. For 174 of these stars, we obtained multi-epoch data with sufficient time baselines to identify binaries based on RV variations. We identified eight close binaries and four close triples, of which three and two, respectively, are new discoveries. The spectroscopic multiplicity fractions we find for Chamaeleon I (7%) and Taurus-Auriga (6%) are similar to each other, and to the results of field star surveys in the same mass and period regime. However, unlike the results from imaging surveys, the frequency of systems with close companions in our sample is not seen to depend on primary mass. Additionally, we do not find a strong correlation between accretion and close multiplicity. This implies that close companions are not likely the main source of the accretion shut down observed in weak-lined T Tauri stars. Our results also suggest that sufficient RV precision can be achieved for at least a subset of slowly rotating young stars to search for hot Jupiter planets.

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 score0.007
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.016
GPT teacher head0.267
Teacher spread0.251 · 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

Citations171
Published2012
Admission routes1
Has abstractyes

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