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Record W4319787570 · doi:10.1088/1475-7516/2023/02/022

Black holes in dRGT massive gravity with the signature of EHT observations of M87*

2023· article· en· W4319787570 on OpenAlexaff
S. H. Hendi, Kh. Jafarzade, B. Eslam Panah

Bibliographic record

VenueJournal of Cosmology and Astroparticle Physics · 2023
Typearticle
Languageen
FieldPhysics and Astronomy
TopicPulsars and Gravitational Waves Research
Canadian institutionsCanadian Quantum Research Center
Fundersnot available
KeywordsPhysicsBlack hole (networking)Massive gravityShadow (psychology)Event horizonTheoretical physicsRotation (mathematics)Strong gravityAstrophysicsEvent (particle physics)Classical mechanicsGravitationGravitonGeometry

Abstract

fetched live from OpenAlex

Abstract The recent Event Horizon Telescope (EHT) observations of the M87* have led to a surge of interest in studying the shadow of black holes. Besides, investigation of time evolution and lifetime of black holes helps us to veto/restrict some theoretical models in gravitating systems. Motivated by such exciting properties, we study optical features of black holes, such as the shadow geometrical shape and the energy emission rate in modified gravity. We consider a charged AdS black hole in dRGT massive gravity and look for criteria to restrict the free parameters of the theory. The main goal of this paper is to compare the shadow of the mentioned black hole in a rotating case with the EHT data to obtain the allowed regions of the model parameters. Therefore, we employ the Newman-Janis algorithm to build the rotating counterpart of static solution in dRGT massive gravity. We also calculate the energy emission rate for the rotating case and discuss how the rotation factor and other parameters affect the emission of particles around the black holes.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.209
Threshold uncertainty score0.162

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 head0.022
GPT teacher head0.310
Teacher spread0.287 · 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 designObservational
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

Citations35
Published2023
Admission routes1
Has abstractyes

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