2377 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.
The authors demonstrate that the self-correcting memory in 3d Wegner gauge theory remains stable against arbitrary small perturbations through a novel "gauge averaging" method, which reveals that the phase is fluctuation-stabilized with robustness that increases with temperature up to a critical point.
This paper demonstrates that flavor-current correlators in strongly-coupled dense holographic matter agree with non-Abelian hydrodynamic predictions in an extended hydrodynamic regime where temperature is much lower than frequency and momentum, and proposes a holographic product formula to capture both hydrodynamic and AdS pole effects.
This study presents the first experimental realization of one-dimensional ballistic aggregation using a droplet train, demonstrating that the process is governed by a single length scale and maintains key scaling characteristics despite air drag-induced correlation inversions, thereby revealing the limitations of mean-field descriptions when collisions involve directional memory.
This paper presents an exact transfer-matrix analysis of a frustrated spin-pseudospin sawtooth chain modeling one-dimensional cuprates, revealing how the competition between spin and charge degrees of freedom drives the evolution of zero-temperature phase boundaries into pronounced thermal pseudo-transitions characterized by sharp thermodynamic anomalies and cooperative charge-magnetic rearrangements.
This paper analytically solves the optimal feedback control problem for a partially observable system undergoing stepwise equilibration, revealing that the minimum work cost balances thermodynamic transport against information-enabled extraction and is ultimately constrained by the cost of measurement to yield a finite optimal number of intervention cycles.
This paper demonstrates that non-Hermitian critical chains near exceptional points exhibit a unique entanglement entropy scaling with an additional term, which arises from the de Sitter geometry of the renormalization flow and signifies that the system's entanglement remains sensitive to small energy gaps even at sub-finite scales, unlike their Hermitian counterparts.
This paper investigates the non-Gaussian behavior of switching fractional Brownian motion by deriving exact analytical expressions for kurtosis and demonstrating its effectiveness, alongside the Hellinger distance, as a metric for detecting deviations from Gaussianity in heterogeneous anomalous diffusion processes.
This paper introduces a telegraphic multifractional Brownian motion model that utilizes a smoothed telegraph process to generate a stationary beta distribution of Hurst exponents, thereby bridging the gap between theoretical formulations and empirical observations in diverse fields such as biology, climate, and finance while offering a methodology for model identification.
This paper reveals that pixel-space diffusion models intrinsically separate information into semantic content and low-level perceptual details, a property that explains how classifier-free guidance effectively amplifies semantic structure early in generation while allowing fine details to emerge later.
This paper establishes that Scrooge ensembles, which represent maximally entropic distributions of pure states under physical constraints, universally emerge in quantum many-body systems through chaotic dynamics and measurements, with their generation requiring resources that scale efficiently with the desired approximation accuracy.