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Record W4416005271 · doi:10.1103/gxm2-trn6

Regulator and gauge dependence of the Abelian gauge coupling in asymptotically safe quantum gravity

2025· article· en· W4416005271 on OpenAlexfundno aff
Maksym Riabokon, Marc Schiffer, Fabian Wagner

Bibliographic record

VenuePhysical review. D/Physical review. D. · 2025
Typearticle
Languageen
FieldPhysics and Astronomy
TopicNoncommutative and Quantum Gravity Theories
Canadian institutionsnot available
FundersInstitut Périmètre de physique théoriqueRadboud UniversiteitGovernment of CanadaMinistry of Colleges and UniversitiesInnovation, Science and Economic Development Canada
KeywordsGeneral relativityGauge (firearms)Abelian groupGauge theoryQuantum gravityCoupling (piping)Asymptotic safety in quantum gravity

Abstract

fetched live from OpenAlex

Both General Relativity and the Standard Model of particle physics are not UV complete. General Relativity is perturbatively nonrenormalizable, while the Standard Model features Landau poles, where couplings are predicted to diverge at finite energies, e.g., in the Abelian gauge sector. Asymptotically safe quantum gravity may resolve both of these issues at the same time. In this paper, we assess the systematic uncertainties associated with this scenario, in particular with the gravitationally induced UV-completion of the Abelian gauge sector. Specifically, we study the dependence of this qualitative feature, namely the existence of a UV-complete gauge sector, on unphysical choices like the gauge, and the regulator function. Intriguingly, in some scenarios, we find simultaneous points of minimal sensitivity relative to both the regulator and gauge parameters, which allow for a UV completion. This provides further indications that the simultaneous UV-completion of quantum gravity and matter via an asymptotically safe fixed point is a robust physical feature, and that physical quantities, like scaling exponents, can become independent of unphysical choices.

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)0.001
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: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.020
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.011
GPT teacher head0.394
Teacher spread0.384 · 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 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

Citations1
Published2025
Admission routes1
Has abstractyes

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