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Record W4366503449 · doi:10.1093/mnras/stad1131

Orbital evolution of binaries in circumbinary discs

2023· article· en· W4366503449 on OpenAlexafffund
Magdalena S. Siwek, Rainer Weinberger, Lars Hernquist

Bibliographic record

VenueMonthly Notices of the Royal Astronomical Society · 2023
Typearticle
Languageen
FieldPhysics and Astronomy
TopicPulsars and Gravitational Waves Research
Canadian institutionsCanadian Institute for Theoretical Astrophysics
FundersNatural Sciences and Engineering Research Council of CanadaGordon and Betty Moore FoundationJohn Templeton FoundationHarvard UniversityNational Science Foundation
KeywordsPhysicsCircumbinary planetMass ratioAstrophysicsEccentricity (behavior)Binary numberGravitational waveParameter spacePopulationCoalescence (physics)AstronomyStars

Abstract

fetched live from OpenAlex

ABSTRACT We present the to-date largest parameter space exploration of binaries in circumbinary discs (CBDs), deriving orbital evolution prescriptions for eccentric, unequal mass binaries from our suite of hydrodynamic simulations. In all cases, binary eccentricities evolve towards steady state values that increase with mass ratio, and saturate at an equilibrium eccentricity eb,eq ∼ 0.5 in the large mass ratio regime, in line with resonant theory. For binaries accreting at their combined Eddington limit, a steady state eccentricity can be achieved within a few megayears. Once at their steady state eccentricities, binaries with qb ≳ 0.3 evolve towards coalescence, while lower mass ratio systems expand due to CBD torques. We discuss implications for population studies of massive black hole binaries, protostars in binary systems, and post-common envelope binaries observed by ground-based gravitational wave detectors.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.015
Threshold uncertainty score0.030

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 studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
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 head0.009
GPT teacher head0.264
Teacher spread0.254 · 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 designSimulation or modeling
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

Citations87
Published2023
Admission routes2
Has abstractyes

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