747 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 applies the Born-Oppenheimer effective field theory, a unified QCD-based framework derived from scale separation and symmetries, to describe the dynamics of ordinary and exotic hadrons containing two heavy quarks, specifically analyzing the , , and their bottom counterparts to elucidate the patterns of XYZ exotic states.
This paper demonstrates through lattice simulations that introducing an explicit Majorana mass to half of a Spin(10)-like generation in a four-dimensional Symmetric Mass Generation (SMG) model does not induce a seesaw mechanism or generate a light mass for the unsourced half, as the exact symmetry of the sourced action forces all unsourced mass terms to average to zero without spontaneous symmetry breaking.
This paper presents a dynamical holographic study of gravitational form factors for scalar, pseudoscalar, and tensor glueballs, demonstrating that while standard constrained cubic vertices fail to match lattice data, a proposed normalized heavy-target effective coupling successfully reproduces scalar observations and provides predictions for tensor and pseudoscalar form factors and pressure profiles.
This paper benchmarks two parameter-matched neural surrogate architectures, Transolver and BSMS-MGN, for approximating linear radiation transport on Lattice and Hohlraum benchmarks, revealing that optimal inductive bias choices are architecture-dependent and that downstream utility relies on per-quantity-of-interest sensitivity rather than a single field-level metric.
This paper presents a phenomenological study combining low- and high-transverse-momentum contributions to estimate the longitudinal unpolarized structure function in SIDIS, revealing that its sizable ratio to the transverse structure function () necessitates its inclusion for accurate interpretation of experimental data and motivates dedicated measurements to further constrain this longitudinal contribution.
This paper establishes a theoretical framework linking hybrid qubit-oscillator experiments to quantum field theory by interpreting characteristic function measurements as Feynman diagrams, and demonstrates through numerical simulations that these diagrams can be accurately reconstructed from Ramsey-type qubit measurements using maximum-likelihood estimation, offering a scalable bottom-up approach for probing lattice field theory simulators.
This paper demonstrates that the thermal branch of black holes in the BMN matrix model terminates at a critical point via a continuous transition to a confined phase, characterized by a universal neck geometry and the absence of the previously hypothesized first-order phase transition.
This paper introduces a polynomial-scaling finite element method for solving partial differential equations by representing the augmented coefficient space of non-linear constraints using entanglement-basis tensor networks, specifically leveraging matrix product states and DMRG sweeps to avoid exponential complexity while ensuring convergence for both steady-state and time-dependent problems.
This paper introduces a stochastic all-mode approach for general tensor renormalization group methods that eliminates systematic errors in coarse-graining by utilizing a "squeezer" construction, demonstrating its effectiveness through higher-order TRG applications to two- and three-dimensional Ising models.
This dissertation advances the first-principles understanding of hadron structure by presenting state-of-the-art lattice QCD calculations of pion and nucleon partonic observables, including transverse-momentum-dependent distributions and the Collins-Soper kernel, while also exploring machine-learning techniques to accelerate lattice simulations.