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Insights from Melvin–Kerr–Newman spacetimes

2015· article· en· W1489646123 on OpenAlexafffund
Ivan Booth, Maribeth E. Hunt, Alberto Palomo Lozano, Hari K. Kunduri

Bibliographic record

VenueClassical and Quantum Gravity · 2015
Typearticle
Languageen
FieldPhysics and Astronomy
TopicBlack Holes and Theoretical Physics
Canadian institutionsMemorial University of Newfoundland
FundersComisión Nacional de Investigación Científica y TecnológicaNatural Sciences and Engineering Research Council of Canada
KeywordsPhysicsMathematical physicsTheoretical physicsClassical mechanics

Abstract

fetched live from OpenAlex

We examine several aspects of black hole horizon physics using the Melvin–Kerr–Newman (MKN) family of spacetimes. Roughly speaking these are black holes immersed in a distorting background magnetic field and unlike the standard Kerr–Newman (KN) family they are not asymptotically flat. As exact solutions with horizons that can be highly distorted relative to KN, they provide a good testbed for ideas about and theorems constraining black hole horizons. We explicitly show that MKN horizons with fixed magnetic field parameters may be uniquely specified by their area, charge and angular momentum and that the charge and angular momentum are bound by horizon area in the same way as for KN. As expected, extremal MKN horizons are geometrically isomorphic to extremal KN horizons and the geometric distortion of near-extremal horizons is constrained by their proximity to extremality. At the other extreme, Melvin–Schwarzschild solutions may be infinitely distorted, however for intermediate cases any non-zero charge or angular momentum restricts distortions to be finite. These properties are in agreement with known theorems but are seen to be satisfied in interesting and non-trivial ways.

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)0.001
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: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.002
Scholarly communication0.0010.003
Open science0.0010.001
Research integrity0.0010.001
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 head0.017
GPT teacher head0.241
Teacher spread0.225 · 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

Citations32
Published2015
Admission routes2
Has abstractyes

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