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Collision of particles and energy extraction in hyperscaling violation background

2023· article· en· W4320916997 on OpenAlexaff
J. Sadeghi, Behnam Pourhassan, Saheb Soroushfar, R. Toorandaz

Bibliographic record

VenueNuclear Physics B · 2023
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAstrophysical Phenomena and Observations
Canadian institutionsCanadian Quantum Research Center
Fundersnot available
KeywordsPenrose processBlack hole (networking)PhysicsExtremal black holeCharged black holeRotating black holeCharge (physics)HorizonGeodesicCenter of mass (relativistic)CollisionQuantum electrodynamicsClassical mechanicsQuantum mechanicsGeometry

Abstract

fetched live from OpenAlex

In this paper, we investigate particle collision near the charged black hole with hyperscaling violating factor under different conditions on mass, charge and θ parameters. In that case, we calculate the center of mass (CM) energy of two colliding particles on the black hole horizon. We find that unlike the other stationary black holes that have been studied so far, the center of mass energy on the horizon can be infinite in certain conditions. By computing the effective potential, we found that particles can collide with very high CM energy on the horizon at two states. The first is an uncharged black hole with small mass and small θ. The second is a massive black hole with the small value of charge and θ. In addition, we investigate the geodesic motion of particles and plot some possible orbits around this black hole. Then, we obtain the ergosphere extent of the hyperscaling violation charged black hole and found that increasing value of θ, the ergoregion limit reduces and we can not extract energy with large value of θ. We also found that energy extraction from the black hole only depends on the absorbed charge by 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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.000
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 head0.027
GPT teacher head0.258
Teacher spread0.230 · 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 designSimulation or modeling
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

Citations0
Published2023
Admission routes1
Has abstractyes

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