MétaCan
Menu
Back to cohort
Record W4386301952 · doi:10.48550/arxiv.2308.14796

Spin dependence of black hole ringdown nonlinearities

2023· preprint· en· W4386301952 on OpenAlexfundno aff
Jaime Redondo–Yuste, G. Carullo, Justin L. Ripley, Emanuele Berti, Vítor Cardoso

Bibliographic record

VenuearXiv (Cornell University) · 2023
Typepreprint
Languageen
FieldEngineering
TopicGeophysics and Sensor Technology
Canadian institutionsnot available
FundersAmaldi Research CenterInstitut Périmètre de physique théoriqueMinistero degli Affari Esteri e della Cooperazione InternazionaleIndo-US Science and Technology ForumHorizon 2020 Framework ProgrammeNational Research FoundationEuropean CommissionJohn Templeton FoundationDanmarks GrundforskningsfondNational Aeronautics and Space AdministrationVillum FondenNational Science Foundation
KeywordsPhysicsSpinsBlack hole (networking)AmplitudeExtrapolationRotating black holeBinary black holeQuasinormal modePerturbation (astronomy)ExcitationCoalescence (physics)Spin (aerodynamics)Quantum electrodynamicsQuantum mechanicsGravitationGravitational waveCondensed matter physicsAngular momentum

Abstract

fetched live from OpenAlex

The nonlinear character of general relativity leaves its imprint in the coalescence of two black holes, from the inspiral to the final ringdown stage. To quantify the impact of nonlinearities, we work at second order in black hole perturbation theory and we study the excitation of second-order modes relative to the first-order modes that drive them as we vary the black hole spin and the initial data for the perturbations. The relative amplitude of second-order modes is only mildly dependent on the initial data that we consider, but it strongly decreases for large black hole spins. This implies that the extrapolation of calculations based on the Kerr-CFT correspondence to subextremal Kerr black holes should be viewed with caution.

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)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.000

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.054
GPT teacher head0.180
Teacher spread0.126 · 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 designTheoretical or conceptual
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

Citations1
Published2023
Admission routes1
Has abstractyes

Explore more

Same venuearXiv (Cornell University)Same topicGeophysics and Sensor TechnologyFrench-language works237,207