Corrections to scaling in the 2D φ4 model: Monte Carlo results and some related problems
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".