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Record W4386721495 · doi:10.48550/arxiv.2309.05775

All near-horizon symmetries of the Schwarzschild black hole in linearised gravity

2023· preprint· en· W4386721495 on OpenAlexfundno aff
Ankit Aggarwal, Nava Gaddam

Bibliographic record

VenueUvA-DARE (University of Amsterdam) · 2023
Typepreprint
Languageen
FieldPhysics and Astronomy
TopicAstrophysical Phenomena and Observations
Canadian institutionsnot available
FundersInstitut Périmètre de physique théoriqueNederlandse Organisatie voor Wetenschappelijk OnderzoekFonds De La Recherche Scientifique - FNRS
KeywordsPhysicsHorizonEvent horizonBlack hole (networking)Homogeneous spaceMathematical physicsSchwarzschild radiusGravitationClassical mechanicsGeometryMathematics

Abstract

fetched live from OpenAlex

Asymptotic symmetries are known to constrain the infrared behaviour of scattering processes in asymptotically flat spacetimes. By the same token, one expects symmetries of the black hole horizon to constrain near-horizon gravitational scattering. In this paper, we take a step towards establishing this connection. We find all near-horizon symmetries that can be potentially relevant to gravitational scattering near the horizon of the Schwarzschild black hole. We study large diffeomorphisms of linearised perturbations of the Schwarzschild black hole in a partial wave basis and in a gauge that allows for gravitational radiation crossing the event horizon. This setup is ideally suited for studying processes involving near-horizon gravitons like scattering and black hole evaporation. We find the most general near-horizon symmetries that are consistent with finite perturbations at the horizon. Since we do not impose any further boundary conditions, these symmetries represent the biggest set of symmetries in this setting. We find the associated covariant charges to be finite and non-zero showing that these symmetries are physical. Interestingly, for a large black hole, the dominant symmetries are just two copies of $ u(1)$.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.313
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.002
Research integrity0.0000.001
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 head0.023
GPT teacher head0.223
Teacher spread0.200 · 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.

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

Citations1
Published2023
Admission routes1
Has abstractyes

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