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Numerical Relativity and Astrophysics

2014· article· en· W2109428817 on OpenAlexaff
Luis Lehner, Frans Pretorius

Bibliographic record

VenueAnnual Review of Astronomy and Astrophysics · 2014
Typearticle
Languageen
FieldPhysics and Astronomy
TopicPulsars and Gravitational Waves Research
Canadian institutionsPerimeter Institute
Fundersnot available
KeywordsPhysicsGeneral relativityGravitational waveNumerical relativityAstrophysicsTwo-body problem in general relativityGravitational fieldTests of general relativityAccretion (finance)Theory of relativityGravitationEinstein field equationsAstronomyBinary black holeBlack hole (networking)Gravitational collapseGravitational time dilationTheoretical physicsIntroduction to the mathematics of general relativity

Abstract

fetched live from OpenAlex

Throughout the Universe many powerful events are driven by strong gravitational effects that require general relativity to fully describe them. These include compact binary mergers, black hole accretion, and stellar collapse, where velocities can approach the speed of light and extreme gravitational fields (Φ Newt /c 2 ≃1) mediate the interactions. Many of these processes trigger emission across a broad range of the electromagnetic spectrum. Compact binaries further source strong gravitational wave emission that could directly be detected in the near future. This feat will open up a gravitational wave window into our Universe and revolutionize our understanding of it. Describing these phenomena requires general relativity, and—where dynamical effects strongly modify gravitational fields—the full Einstein equations coupled to matter sources. Numerical relativity is a field within general relativity concerned with studying such scenarios that cannot be accurately modeled via perturbative or analytical calculations. In this review, we examine results obtained within this discipline, with a focus on its impact in astrophysics.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.946
Threshold uncertainty score0.812

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.007
GPT teacher head0.296
Teacher spread0.289 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designOther design
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

Citations129
Published2014
Admission routes1
Has abstractyes

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