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Record W2767158222 · doi:10.3847/1538-4357/aaa765

Stellar Multiplicity Meets Stellar Evolution and Metallicity: The APOGEE View

2018· article· en· W2767158222 on OpenAlexaff

Bibliographic record

VenueThe Astrophysical Journal · 2018
Typearticle
Languageen
FieldPhysics and Astronomy
TopicStellar, planetary, and galactic studies
Canadian institutionsCanadian Institute for Theoretical AstrophysicsUniversity of Toronto
Fundersnot available
KeywordsStarsMilky WayRed-giant branchStellar evolutionCircumbinary planetStellar collisionRed clumpStellar kinematicsBinary starMain sequence

Abstract

fetched live from OpenAlex

Abstract We use the multi-epoch radial velocities acquired by the Apache Point Observatory Galactic Evolution Experiment (APOGEE) survey to perform a large-scale statistical study of stellar multiplicity for field stars in the Milky Way, spanning the evolutionary phases between the main sequence (MS) and the red clump. We show that the distribution of maximum radial velocity shifts (ΔRV max ) for APOGEE targets is a strong function of log g , with MS stars showing ΔRV max as high as ∼300 , and steadily dropping down to ∼30 for log g ∼ 0, as stars climb up the red giant branch (RGB). Red clump stars show a distribution of ΔRV max values comparable to that of stars at the tip of the RGB, implying they have similar multiplicity characteristics. The observed attrition of high ΔRV max systems in the RGB is consistent with a lognormal period distribution in the MS and a multiplicity fraction of 0.35, which is truncated at an increasing period as stars become physically larger and undergo mass transfer after Roche Lobe overflow during H-shell burning. The ΔRV max distributions also show that the multiplicity characteristics of field stars are metallicity-dependent, with metal-poor ([Fe/H] ≲ −0.5) stars having a multiplicity fraction a factor of 2–3 higher than metal-rich ([Fe/H] ≳ 0.0) stars. This has profound implications for the formation rates of interacting binaries observed by astronomical transient surveys and gravitational wave detectors, as well as the habitability of circumbinary 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 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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.326
Threshold uncertainty score0.897

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 studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.014
GPT teacher head0.233
Teacher spread0.219 · 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

Citations135
Published2018
Admission routes1
Has abstractyes

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