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Record W2412987816 · doi:10.1103/physrevd.94.064009

Toroidal horizons in binary black hole mergers

2016· article· en· W2412987816 on OpenAlexfundno aff
Andy Bohn, Saul A. Teukolsky

Bibliographic record

VenuePhysical review. D/Physical review. D. · 2016
Typearticle
Languageen
FieldPhysics and Astronomy
TopicPulsars and Gravitational Waves Research
Canadian institutionsnot available
FundersGovernment of OntarioUniversity of TorontoCanada Foundation for InnovationSherman Fairchild FoundationNational Science Foundation
KeywordsEvent horizonToroidTopology (electrical circuits)PhysicsBlack hole (networking)HorizonTorusClassical mechanicsEvent (particle physics)GeometryMathematicsComputer scienceQuantum mechanics

Abstract

fetched live from OpenAlex

We find the first binary black hole event horizon with a toroidal topology. It has been predicted that generically the event horizons of merging black holes should briefly have a toroidal topology. However, such a phase has never been seen in numerical simulations. Instead, in all previous simulations, the topology of the event horizon transitions directly from two spheres during the inspiral to a single sphere as the black holes merge. We find a coordinate transformation to a foliation of spacelike hypersurfaces that ``cut a hole'' through the event horizon surface, resulting in a toroidal event horizon, thus reconciling the numerical work with theoretical expectations. The demonstration requires extremely high numerical precision, which is made possible by a new event horizon code described in a companion paper. A torus could potentially provide a mechanism for violating topological censorship. However, these toroidal event horizons satisfy topological censorship by construction, because we can always trivially apply the inverse coordinate transformation to remove the topological feature.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.006

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 science0.0000.001
Research integrity0.0010.000
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 head0.017
GPT teacher head0.454
Teacher spread0.437 · 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

Citations14
Published2016
Admission routes1
Has abstractyes

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