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Record W3089335788 · doi:10.21468/scipostphys.12.2.046

Constraints on beta functions in field theories

2022· preprint· en· W3089335788 on OpenAlexafffund
Han Ma, Sung-Sik Lee

Bibliographic record

VenueSciPost Physics · 2022
Typepreprint
Languageen
FieldPhysics and Astronomy
TopicBlack Holes and Theoretical Physics
Canadian institutionsMcMaster UniversityPerimeter Institute
FundersMinistry of Colleges and UniversitiesNatural Sciences and Engineering Research Council of CanadaGovernment of Canada
KeywordsRenormalizationRenormalization groupSubspace topologyBeta function (physics)BETA (programming language)Flow (mathematics)Functional renormalization groupMathematical physicsPhysicsQuantum field theorySpace (punctuation)Field (mathematics)MathematicsTheoretical physicsQuantum mechanicsQuantumPure mathematicsMathematical analysisQuantum gravityComputer scienceThermal quantum field theory

Abstract

fetched live from OpenAlex

The \beta β -functions describe how couplings run under the renormalization group flow in field theories. In general, all couplings that respect the symmetry and locality are generated under the renormalization group flow, and the exact renormalization group flow is characterized by the \beta β -functions defined in the infinite dimensional space of couplings. In this paper, we show that the renormalization group flow is highly constrained so that the \beta β -functions defined in a measure zero subspace of couplings completely determine the \beta β -functions in the entire space of couplings. We provide a quantum renormalization group-based algorithm for reconstructing the full \beta β -functions from the \beta β -functions defined in the subspace. As examples, we derive the full \beta β -functions for the O(N) O ( N ) vector model and the O_L(N) \times O_R(N) O L ( N ) × O R ( N ) matrix model entirely from the \beta β -functions defined in the subspace of single-trace couplings.

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)0.006
metaresearch head score (Gemma)0.017
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.008
Threshold uncertainty score0.032

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.017
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.001
Science and technology studies0.0030.005
Scholarly communication0.0060.010
Open science0.0020.004
Research integrity0.0030.005
Insufficient payload (model declined to judge)0.0080.002

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 head0.012
GPT teacher head0.260
Teacher spread0.248 · 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 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

Citations4
Published2022
Admission routes2
Has abstractyes

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