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Record W4310159121 · doi:10.1103/physrevd.106.104055

Binary neutron star mergers in Einstein-scalar-Gauss-Bonnet gravity

2022· article· en· W4310159121 on OpenAlexafffund
William E. East, Frans Pretorius

Bibliographic record

VenuePhysical review. D/Physical review. D. · 2022
Typearticle
Languageen
FieldPhysics and Astronomy
TopicPulsars and Gravitational Waves Research
Canadian institutionsPerimeter Institute
FundersNatural Sciences and Engineering Research Council of CanadaOntario Ministry of Research, Innovation and ScienceNational Science FoundationGovernment of CanadaInstitut Périmètre de physique théoriqueCanadian Institute for Advanced ResearchSimons Foundation
KeywordsPhysicsNeutron starBlack hole (networking)Gravitational waveScalar (mathematics)General relativityAstrophysicsStellar black holeBinary black holeScalar fieldQuantum electrodynamicsClassical mechanics

Abstract

fetched live from OpenAlex

Binary neutron star mergers, which can lead to less massive black holes relative to other known astrophysical channels, have the potential to probe modifications to general relativity that arise at smaller curvature scales compared to more massive compact object binaries. As a representative example of this, here we study binary neutron star mergers in shift-symmetric Einstein-scalar-Gauss-Bonnet gravity using evolutions of the full, nonperturbative evolution equations. We find that the impact on the inspiral is small, even at large values of the modified gravity coupling (as expected, as neutron stars do not have scalar charge in this theory). However, postmerger there can be strong scalar effects, including radiation. When a black hole forms, it develops scalar charge, impacting the ringdown gravitational wave signal. In cases where a longer-lived remnant star persists postmerger, we find that the oscillations of the star source levels of scalar radiation similar to the black hole formation cases. In remnant stars, we further find that at coupling values comparable to the maximum value for which black hole solutions of the same mass exist, there is significant nonlinear enhancement in the scalar field, which if sufficiently large lead to a breakdown in the evolution, seemingly due to loss of hyperbolicity of the underlying equations.

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)0.000
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 score0.004

Distilled classifier scores by category (both heads)

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 communication0.0010.000
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 head0.016
GPT teacher head0.445
Teacher spread0.429 · 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

Citations38
Published2022
Admission routes2
Has abstractyes

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