749 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 presents recent lattice QCD results for hadronic screening masses at temperatures extending from the GeV scale to the electroweak scale, revealing persistent non-perturbative higher-order effects that challenge purely perturbative predictions and offer new insights into the microscopic structure of QCD under extreme thermal conditions.
This paper demonstrates that the choice of prescription in the Collins-Soper-Sterman framework introduces a significant intrinsic theoretical uncertainty in Transverse Momentum Dependent distribution extractions, yielding consistent results for low-energy data but causing substantial discrepancies in intermediate-to-high energy predictions.
This study employs QCD light-cone sum rules to calculate the magnetic dipole moments of hidden-charm pentaquarks using four distinct diquark-diquark-antiquark interpolating currents, demonstrating that these electromagnetic observables are highly sensitive to internal quark configurations and can effectively discriminate between compact and molecular structural models.
This paper evaluates a machine learning framework and a novel multipoint method for the ill-posed spectral reconstruction problem by comparing them against established techniques on mock data, then applying the most effective approaches to quenched lattice data to extract the electric conductivity in the presence of a non-zero external magnetic field.
This paper introduces HMC, a hybrid algorithm combining Exact Diagonalization and Hamiltonian Monte Carlo that overcomes the scaling limits of ED and the sign problem of standard Monte Carlo methods to enable efficient simulations of larger 2D arrays of coupled quantum wires.
This paper presents the status of an exploratory lattice QCD calculation utilizing a dual heavy-quark strategy and the finite-volume formalism to determine the hadronic matrix elements for the resonant final state, a crucial step toward achieving precise Standard Model predictions for the decay.
The paper introduces CaRBM, a fixed-depth quantum algorithm that utilizes Restricted Boltzmann Machine block-encoding with partial correction to efficiently prepare thermal states, particularly at high temperatures, as demonstrated by its application to calculating partition function zeros and phase diagrams in the XXZ and Gross-Neveu models.
This paper proposes a generic approach using spontaneous symmetry breaking to construct 3D lattice models that evade the no-go theorem and yield a single Weyl fermion in the infrared limit, demonstrating that these models form an equivalent class with gapless superconductors and superfluids across three distinct symmetry-breaking pathways.
This paper presents next-to-next-to-leading order (NNLO) QCD evaluations of proton and pion mass decompositions using gravitational form factors, comparing standard quark/gluon and trace-anomaly breakdowns with a novel twist-two/twist-four separation to demonstrate its advantages and reveal distinct parton-correlation behaviors between the two hadrons.
This paper investigates practical optimizations for the Harrow-Hassidim-Lloyd (HHL) algorithm on near-term quantum simulators, demonstrating that while Suzuki-Trotter decomposition and block-encoding strategies improve performance for sparse and moderately dense matrices respectively, the algorithm's fidelity and scalability are fundamentally constrained by matrix sparsity and condition number.