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2D φ⁴ lattice field configurations

Monte Carlo configurations of the two-dimensional scalar φ⁴ theory on periodic square lattices, generated for training and benchmarking generative models (score-based diffusion), and for unsupervised-learning studies of the phase transition (PCA).

Physics

Lattice action in the hopping-parameter form

S[ϕ]=x[2κμ=1,2ϕxϕx+μ^+(12λ)ϕx2+λϕx4]S[\phi] = \sum_x \Big[ -2\kappa \sum_{\mu=1,2} \phi_x \phi_{x+\hat\mu} + (1-2\lambda)\phi_x^2 + \lambda \phi_x^4 \Big]

with quartic coupling fixed at λ = 0.022. The three hopping parameters straddle the phase transition (κ_c ≈ 0.2708 for this λ):

κ phase
0.26 symmetric (disordered)
0.2705 near-critical
0.28 broken (ordered)

Files

15 .npz files, one per (κ, L) pair:

cfgs_wolff_fahmc_k=<kappa>_l=0.022_<L>^2.npz,  L ∈ {8, 16, 32, 64, 128}

Each file contains 10240 independent configurations. Keys:

key content
cfgs float64 array, shape (10240, L, L)
κ, λ hopping parameter, quartic coupling
N, n_samples lattice size L, number of configurations
acc_rate HMC acceptance rate
ε_final final tuned HMC step size

Total size ≈ 5 GB (the L=128 files are 1.3 GB each).

Generation

Sampled with alternating Wolff cluster updates and Fourier-accelerated HMC (wolff_fahmc), which keeps autocorrelations short even near criticality. Configurations within a file are saved after thermalization and thinned so they are effectively independent.

Usage

import numpy as np

d = np.load("cfgs_wolff_fahmc_k=0.2705_l=0.022_32^2.npz")
phi = d["cfgs"]            # (10240, 32, 32)
M = phi.mean(axis=(1, 2))  # per-configuration magnetization

pca_phi4.py reproduces the PCA analysis figures (pca_phi4_L32.png, pca_phi4_L128.png): the PCA spectrum equals the sorted momentum-space propagator G(k) = ⟨|φ(k)|²⟩, PC1 = L·M is the magnetization mode, and PC2–PC5 span the lowest-momentum plane-wave quadruplet.

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