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Record W3126482351 · doi:10.3847/2041-8213/abf566

Multimessenger Signals from Black Hole–Neutron Star Mergers without Significant Tidal Disruption

2021· article· en· W3126482351 on OpenAlexaff

Bibliographic record

VenueThe Astrophysical Journal Letters · 2021
Typearticle
Languageen
FieldPhysics and Astronomy
TopicPulsars and Gravitational Waves Research
Canadian institutionsPerimeter Institute
Fundersnot available
KeywordsNeutron starBlack hole (networking)Accretion (finance)PlasmoidMagnetic fieldMagnetosphereAstrophysical jetLuminosity

Abstract

fetched live from OpenAlex

Abstract We study the multimessenger signals from the merger of a black hole with a magnetized neutron star using resistive magnetohydrodynamics simulations coupled to full general relativity. We focus on a case with a 5:1 mass ratio, where only a small amount of the neutron star matter remains post-merger, but we nevertheless find that significant electromagnetic radiation can be powered by the interaction of the neutron star’s magnetosphere with the black hole. In the lead-up to merger, strong twisting of magnetic field lines from the inspiral leads to plasmoid emission and results in a luminosity in excess of that expected from unipolar induction. We find that the strongest emission occurs shortly after merger during a transitory period in which magnetic loops form and escape the central region. The remaining magnetic field collimates around the spin axis of the remnant black hole before dissipating, an indication that, in more favorable scenarios (higher black hole spin/lower mass ratio) with larger accretion disks, a jet would form.

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)0.001
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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
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 head0.014
GPT teacher head0.295
Teacher spread0.281 · 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

Citations26
Published2021
Admission routes1
Has abstractyes

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