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Record W7110627308

Corrections to scaling in the 2D φ4 model: Monte Carlo results and some related problems

2024· article· en· W7110627308 on OpenAlexfundno aff

Bibliographic record

VenueBIRD (Basque Center for Applied Mathematics) · 2024
Typearticle
Languageen
FieldPhysics and Astronomy
TopicTheoretical and Computational Physics
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of CanadaRīgas Tehniskā Universitāte
KeywordsScalingMonte Carlo methodUniversality (dynamical systems)Renormalization groupConsistency (knowledge bases)Feynman diagramExponentCoulombRenormalization
DOInot available

Abstract

fetched live from OpenAlex

Monte Carlo (MC) simulations have been performed to refine the estimation of the correction-to-scaling exponent ω in the 2D φ 4 model, which belongs to one of the most fundamental universality classes. If corrections have the form ∝ L −ω, then we find ω = 1.546(30) and ω = 1.509(14) as the best estimates. These are obtained from the finite-size scaling of the susceptibility data in the range of linear lattice sizes L ∈ [128, 2048] at the critical value of the Binder cumulant and from the scaling of the corresponding pseudocritical couplings within L ∈ [64, 2048]. These values agree with several other MC estimates at the assumption of the power-law corrections and are comparable with the known results of the ϵ-expansion. In addition, we have tested the consistency with the scaling corrections of the form ∝ L −4/3 , ∝ L −4/3 lnL and ∝ L −4/3/ lnL, which might be expected from some considerations of the renormalization group and Coulomb gas model. The latter option is consistent with our MC data. Our MC results served as a basis for a critical reconsideration of some earlier theoretical conjectures and scaling assumptions. In particular, we have corrected and refined our previous analysis by grouping Feynman diagrams. The renewed analysis gives ω ≈ 4 − d − 2η as some approximation for spatial dimensions d < 4, or ω ≈ 1.5 in two dimensions.

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: none
Teacher disagreement score0.590
Threshold uncertainty score0.430

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 head0.013
GPT teacher head0.243
Teacher spread0.231 · 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

Citations0
Published2024
Admission routes1
Has abstractyes

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