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Record W4321081950 · doi:10.1103/physrevd.107.044031

Lensing signatures of a slowly accelerated black hole

2023· article· en· W4321081950 on OpenAlexafffund
Amjad Ashoorioon, Mohammad Bagher Jahani Poshteh, Robert B. Mann

Bibliographic record

VenuePhysical review. D/Physical review. D. · 2023
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAstrophysical Phenomena and Observations
Canadian institutionsPerimeter InstituteUniversity of Waterloo
FundersNatural Sciences and Engineering Research Council of CanadaIran Science Elites FederationH2020 Marie Skłodowska-Curie ActionsUniversity of Guilan
KeywordsPhysicsBlack hole (networking)Supermassive black holeGeneral relativityAstrophysicsBinary black holeRotating black holeAccelerationIntermediate-mass black holeSpin-flipGalaxyClassical mechanicsGravitational waveAccretion (finance)

Abstract

fetched live from OpenAlex

Accelerating black holes, connected to cosmic strings, could evolve to supermassive black holes. However, if they are going to take part in structure formation and reside at the center of galaxies, their acceleration should be small. This slow acceleration does not change the shadow or image position in a gravitational lensing effect significantly. However, we show that the time delay associated with these images does change significantly. This is in contrast to when the theory governing the strong gravitational field around the black hole is different from general relativity, where not only the differential time delays but the angular position of images would be different. We conclude that, if the observed angular position of images is compatible with the prediction of general relativity, a possible deviation in the differential time delays between the observed values and those predicted by general relativity could be due to the acceleration of the black hole.

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 score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.033
GPT teacher head0.402
Teacher spread0.369 · 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

Citations10
Published2023
Admission routes2
Has abstractyes

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