arXiv subjects

Octavio Vega

Publications and source records attributed to Octavio Vega.

4 recordsLinked to original sources

arXiv2026

Sampling the Schwinger Model with Gauge-Equivariant Diffusion

We present a first study of a diffusion-based approach to accelerated sampling of the $N_f = 2$ lattice Schwinger model. Our work is inspired by recent and growing successes in developing such generative models for ensemble generation in LFT to overcome the well-known critical slowing down problem. We train a U(1)-equivariant score-based generative model to sample gauge link configurations from the marginal Schwinger model. By computing model likelihoods, we obtain unbiased estimates for observables that closely match those produced by MCMC simulations. We also demonstrate improvement over HMC as measured qualitatively by a reduction in topological freezing near critical parameters.

hep-lat↗

arXiv2025

Spectral Diffusion for Sampling on ${\rm SU}(N)$

Although ensemble generation remains a central challenge in lattice field theory simulations, recent advances in generative modeling may offer a path to accelerated sampling in these contexts. In this work, we implement a framework for efficiently training diffusion models acting on ${\rm SU}(N)$ degrees of freedom, adapting the traditional score matching technique to the group manifold. We demonstrate that our models can effectively reproduce several target densities, resulting in precise unbiased expectation values. These results mark a step for diffusion models towards modeling full ${\rm SU}(N)$ lattice field theories, including lattice Quantum Chromodynamics.

hep-lat↗

arXiv2025

Group-Equivariant Diffusion Models for Lattice Field Theory

Near the critical point, Markov Chain Monte Carlo (MCMC) simulations of lattice quantum field theories (LQFT) become increasingly inefficient due to critical slowing down. In this work, we investigate score-based symmetry-preserving diffusion models as an alternative strategy to sample two-dimensional $\phi^4$ and ${\rm U}(1)$ lattice field theories. We develop score networks that are equivariant to a range of group transformations, including global $\mathbb{Z}_2$ reflections, local ${\rm U}(1)$ rotations, and periodic translations $\mathbb{T}$. The score networks are trained using an augmented training scheme, which significantly improves sample quality in the simulated field theories. We also demonstrate empirically that our symmetry-aware models outperform generic score networks in sample quality, expressivity, and effective sample size.

hep-lat↗

arXiv2024

Using AI for Efficient Statistical Inference of Lattice Correlators Across Mass Parameters

Lattice QCD is notorious for its computational expense. Modern lattice simulations require large-scale computational resources to handle the large number of Dirac operator inversions used to construct correlation functions. Machine learning (ML) techniques that can increase, at the analysis level, the information inferred from the correlation functions would therefore be beneficial. We apply supervised learning to infer two-point lattice correlation functions at different target masses. Our work proposes a new method for separating data into training and bias correction subsets for efficient uncertainty estimation. We also benchmark our ML models against a simple ratio method.

hep-lat↗