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Record W1979908178 · doi:10.1088/0004-637x/742/2/90

ELECTROMAGNETIC EXTRACTION OF ENERGY FROM BLACK-HOLE–NEUTRON-STAR BINARIES

2011· article· en· W1979908178 on OpenAlexaff

Bibliographic record

VenueThe Astrophysical Journal · 2011
Typearticle
Languageen
FieldPhysics and Astronomy
TopicPulsars and Gravitational Waves Research
Canadian institutionsInstitute of Particle Physics
Fundersnot available
KeywordsNeutron starGravitational wavePoynting vectorLIGOX-ray bursterBlack hole (networking)Stellar black holeExotic starX-ray binary

Abstract

fetched live from OpenAlex

The coalescence of black-hole–neutron-star binaries is expected to be a principal source of gravitational waves for the next generation of detectors, Advanced LIGO and Advanced Virgo. For black hole masses not much larger than the neutron star mass, the tidal disruption of the neutron star by the black hole provides one avenue for generating an electromagnetic counterpart. However, in this work, we demonstrate that, for all black-hole–neutron-star binaries observable by Advanced LIGO/Virgo, the interaction of the black hole with the magnetic field of the neutron star will generate copious luminosity, comparable to supernovae and active galactic nuclei. This novel effect may have already been observed as a new class of very short gamma-ray bursts by the Swift Gamma-Ray Burst Telescope. These events may be observable to cosmological distances, so that any black-hole–neutron-star coalescence detectable with gravitational waves by Advanced LIGO/Virgo could also be detectable electromagnetically.

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: Theoretical or conceptual · 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
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.001

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 head0.275
Teacher spread0.261 · 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

Citations99
Published2011
Admission routes1
Has abstractyes

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