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Record W4214694925 · doi:10.1088/1361-6382/ac51e7

Homology conditions for RT surfaces in double holography

2022· article· en· W4214694925 on OpenAlexafffund
Dominik Neuenfeld

Bibliographic record

VenueClassical and Quantum Gravity · 2022
Typearticle
Languageen
FieldPhysics and Astronomy
TopicBlack Holes and Theoretical Physics
Canadian institutionsPerimeter Institute
FundersGovernment of CanadaSimons Foundation
KeywordsPhysicsEntropy (arrow of time)HolographyWormholeVon Neumann entropyComputationTheoretical physicsGravitationVon Neumann architectureMathematical physicsStatistical physicsPure mathematicsQuantumClassical mechanicsQuantum mechanicsQuantum entanglementAlgorithmMathematics

Abstract

fetched live from OpenAlex

Abstract Recently, a novel formula for computing entropy in theories coupled to semi-classical gravity has been devised. Using this so-called island formula the entropy of semi-classical black holes follows a Page curve. Here, we study the relation between this novel entropy and semi-classical entropy in the context of doubly-holographic models. Double holography allows for two different d -dimensional descriptions of a black hole coupled to a non-gravitational bath, both of which allow a holographic computation of von Neumann entropy in bath subregions. We argue that the correct homology constraint for Ryu–Takayanagi surfaces depends on which of those d -dimensional perspectives is taken. As a consequence the von Neumann entropies of a fixed subregion in both descriptions can disagree. We discuss how the von Neumann entropies in both descriptions are related to the entropy computed by the island formula and coarse grained entropy. Moreover, we argue that the way operators transform between the two descriptions depends on their complexity. A simple toy model is introduced to demonstrate that a sufficiently complicated map between two descriptions of the system can give rise to an island formula and wormholes. Lastly, we speculate about the relation between double-holography and black hole complementarity.

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: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.378

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 head0.015
GPT teacher head0.270
Teacher spread0.255 · 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 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

Citations30
Published2022
Admission routes2
Has abstractyes

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