1536 papers
Statistical mechanics explores how the chaotic motion of countless tiny particles gives rise to the predictable laws governing heat, pressure, and phase transitions. This field bridges the gap between the microscopic world of atoms and the macroscopic reality we experience daily, offering deep insights into why materials behave the way they do.
On Gist.Science, we process every new preprint in this category as it appears on arXiv to make these complex findings accessible to everyone. For each paper, we provide both a plain-language explanation for the curious reader and a detailed technical summary for specialists, ensuring that groundbreaking research is never lost behind a wall of jargon.
Below are the latest papers in statistical mechanics, freshly curated and summarized to help you understand the cutting edge of this fascinating discipline.
This paper introduces the Correlation-Analysis-based Hypergraph Reconstruction (CAHR) algorithm, a globally consistent framework that accurately reconstructs fault topologies from experimental syndrome statistics without false positives, thereby enabling a practical two-stage inference paradigm for characterizing and decoding highly correlated noise in quantum error correction.
This chapter provides a comprehensive review of entropy foundations and their extensions to complex systems, covering generalized entropies, statistical mechanics, axiomatic frameworks, and dynamic processes to demonstrate that the appropriate choice of entropy depends on a system's specific structure and physical properties.
This study uses Monte Carlo simulations to demonstrate that while total and spin-flip persistence probabilities in a two-dimensional Ising model with competing nonconserved and conserved dynamics follow universal power-law decay and a standard scaling relation, the composite persistence probability exhibits a strong dependence on the move probability that breaks this scaling relation.
This paper derives the exact time-space correlation function for the finite-length semi-open Glauber-Ising model quenched to zero temperature, enabling the calculation of the dual coagulation-diffusion process's empty-interval probability and confirming consistency with dynamical finite-size scaling theory.
This paper proposes a graph-theoretic framework for urban Distributed Acoustic Sensing (DAS) that identifies critical coverage thresholds, demonstrating that even low-density networks can enable earthquake early warning and activity tracking while ensuring privacy, with full city-scale monitoring and individual tracking only achievable beyond a 51.6% coverage percolation threshold.
This paper establishes bounds on the mixing times for the facilitated simple exclusion process on segments and circles, demonstrating that the symmetric variant exhibits pre-cutoff with mixing times of order while the asymmetric variant can display exponentially slow convergence to ergodic components depending on initial conditions, all proven via novel lattice path couplings.
This paper introduces polynomial-depth duality transformations to efficiently prepare Gibbs states for quantum Hamiltonians, such as the 2D toric code, by mapping them to dual classical systems like Ising chains while preserving key mixing properties under Lindbladian dynamics.
This paper establishes an entropic framework for analyzing spontaneous symmetry breaking in equivariant dynamical systems, distinguishing between local mechanisms characterized by increased Shannon entropy and critical slowing down, and global mechanisms where entropy changes depend on the redistribution of probability across symmetry-related sectors.
This paper investigates the collective dynamics of particles diffusing outside a spherical surface where they undergo autocatalytic replication, revealing a rich phase diagram of extinction, steady-state, and growth regimes in three or higher dimensions and providing an explicit analytical description of the population size statistics and its slow power-law convergence to steady state.
This paper investigates the bipartite entanglement entropy of Bethe states across various integrable spin chains, systematically identifying the specific solutions that minimize and maximize entanglement, revealing that while the ground state often minimizes entropy in the XXX model, this correspondence breaks down in higher-spin and non-compact chains, and further developing an optimization algorithm to explore maximum entanglement for off-shell states.