Datasets:
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
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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