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Record W3208504829 · doi:10.1103/physrevd.104.084084

Ultimate fate of apparent horizons during a binary black hole merger. II. The vanishing of apparent horizons

2021· article· en· W3208504829 on OpenAlexafffund
Daniel Pook-Kolb, Ivan Booth, Robie A. Hennigar

Bibliographic record

VenuePhysical review. D/Physical review. D. · 2021
Typearticle
Languageen
FieldPhysics and Astronomy
TopicPulsars and Gravitational Waves Research
Canadian institutionsUniversity of WaterlooWilfrid Laurier UniversityMemorial University of Newfoundland
FundersNatural Sciences and Engineering Research Council of CanadaAsian Office of Aerospace Research and Development
KeywordsNew horizonsBlack hole (networking)SpinningHorizonBinary numberStability (learning theory)GeologyPhysicsGeometryMathematicsChemistryComputer scienceAstronomy

Abstract

fetched live from OpenAlex

In this second part of a two-part paper, we discuss numerical simulations of a head-on merger of two nonspinning black holes. We resolve the fate of the original two apparent horizons by showing that after intersecting, their world tubes ``turn around'' and continue backwards in time. Using the method presented in the first paper [Phys. Rev. D 084083 (2021)] to locate these surfaces, we resolve several such world tubes evolving and connecting through various bifurcations and annihilations. This also draws a consistent picture of the full merger in terms of apparent horizons, or more generally, marginally outer trapped surfaces (MOTSs). The MOTS stability operator provides a natural mechanism to identify MOTSs which should be thought of as black hole boundaries. These are the two initial ones and the final remnant. All other MOTSs lie in the interior and are neither stable nor inner trapped.

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)0.003
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 score0.005
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.002
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
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 head0.020
GPT teacher head0.437
Teacher spread0.417 · 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

Citations25
Published2021
Admission routes2
Has abstractyes

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