630 papers
Hep-Lat, short for High Energy Physics – Lattice, explores the fundamental forces of nature by simulating particle interactions on a digital grid. Instead of relying solely on abstract equations, researchers in this field use powerful computers to model how quarks and gluons bind together, offering deep insights into the structure of matter that are often impossible to derive analytically.
Gist.Science ensures these complex discoveries from arXiv remain accessible to everyone. We process every new preprint in this category as it is posted, providing both plain-language explanations for the curious and detailed technical summaries for experts. This dual approach bridges the gap between cutting-edge simulation work and broader scientific understanding.
Below are the latest papers in High Energy Physics – Lattice, curated directly from arXiv and ready for you to explore.
This paper reviews recent advancements in tensor network renormalization group methods, highlighting their potential to overcome sign and complex action problems in lattice field theories and their growing application to studying quantum chromodynamics at finite temperature and density, as well as universal critical behaviors via conformal field theory insights.
This paper introduces a novel generative architecture based on physics-informed kernels that resolves sign problems and enables efficient sampling for systems with complex probability distributions by mapping them to sign-problem-free manifolds, demonstrating its effectiveness in zero-dimensional field theories and real-time quantum-mechanical evolution.
Using matrix product state simulations, this paper reveals that doping the Gross-Neveu-Wilson model induces exotic inhomogeneous phases, including topological crystals and soliton lattices driven by Hilbert-space fragmentation, as well as chiral spirals, thereby demonstrating the rich finite-density landscape of lattice field theories and motivating future quantum simulations.
Using lattice Yang-Mills simulations, the paper demonstrates that a test quark near a reflective chromometallic boundary in the QCD confinement phase forms a partially confined "quarkiton" state bound by an attractive Cornell-type potential with reduced string tension, analogous to surface excitons in condensed matter physics.
This paper introduces a position-space sampling method within the distillation framework to efficiently compute local multiquark operators, demonstrating that including local tetraquark operators significantly alters the extracted finite-volume spectrum and scattering phase shifts of the state.
This paper presents a high-precision lattice QCD determination of the nucleon iso-vector scalar and tensor charges at the physical point by employing a novel "blending" method to suppress excited state contamination across 15 ensembles, yielding the most accurate predictions to date with comprehensive systematic error analysis.
This paper introduces a non-perturbative lattice Monte Carlo method in imaginary time, featuring a novel sampling technique to overcome false vacuum suppression and a new Fermi's Golden Rule-like formula, to accurately calculate quantum tunneling rates, as validated by reproducing one-dimensional Schrödinger equation results.
This paper proposes a family of quantum walks that eliminate fermion and pseudo-doublers while simulating the Dirac equation in the continuum limit by allowing a non-zero probability for the walker to remain at the same point, despite retaining a small number of non-Dirac low-energy solutions.
This paper introduces a new Monte Carlo estimator for flow fields that utilizes coupled Langevin noise to significantly mitigate statistical noise, thereby offering a robust method for addressing critical slowing down and signal-to-noise issues in lattice field theory while also generating unbiased training data for machine learning.
This paper argues that while classical lattice QCD suffices for stable hadron masses, quantum computers offer distinct utility for calculating resonance masses and nuclear properties by overcoming classical barriers like the Maiani-Testa theorem and signal-to-noise issues, a conclusion reached through extensive collaboration with the AI model Claude.