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Record W2442816495 · doi:10.1093/mnras/stw2786

Stability of mass transfer from massive giants: double black hole binary formation and ultraluminous X-ray sources

2016· article· en· W2442816495 on OpenAlexaff
K. Pavlovskii, Natalia Ivanova, Krzysztof Belczyński, Kenny Van

Bibliographic record

VenueMonthly Notices of the Royal Astronomical Society · 2016
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAstrophysical Phenomena and Observations
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsPhysicsAstrophysicsCommon envelopeLuminosityBlack hole (networking)Mass transferAstronomyPopulationStellar massBinary numberStarsStar formationGalaxyWhite dwarfThermodynamics

Abstract

fetched live from OpenAlex

Mass transfer in binaries with massive donors and compact companions, when the donors rapidly evolve after their main sequence, determines the formation rates of merging double stellar-mass black hole (BH) binaries formed outside clusters. This mass transfer was previously postulated to be unstable and was expected to lead to a common envelope event. The common envelope event then ends with either the merger of the two stars or formation of a binary that eventually may become a merging double BH. We revisit the stability of this mass transfer and find an unanticipated third outcome: for a large range of binary orbital separations, this mass transfer is stable. This newly found stability allows us to reconcile the empirical rate obtained by LIGO, 9-240 Gpc−3 yr−1, with the theoretical rate for double BH binary mergers predicted by population synthesis studies by excluding a channel that predicts a merger rate above 1000 Gpc−3 yr−1. Furthermore, the stability of the mass transfer leads to the formation of ultraluminous X-ray sources. The theoretically predicted formation rates of bright ultraluminous X-ray sources powered by a stellar-mass BH are high enough to explain the number of observed bright ultraluminous X-ray sources.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.453
Threshold uncertainty score0.424

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 studies0.0000.001
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 head0.011
GPT teacher head0.195
Teacher spread0.183 · 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 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

Citations179
Published2016
Admission routes1
Has abstractyes

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