Random Coarse-Graining with Applications to Measuring Irreversibility

Abstract

Thermodynamic irreversibility is a fundamental concept in statistical physics, yet its experimental measurement remains challenging, especially for complex systems. At the same time, imprecise readout is ubiquitous in experiments, but its effect on thermodynamic irreversibility has not been formulated in a general way. We introduce a novel random coarse-graining framework that incorporates a probabilistic mapping from fine-grained to coarse-grained states, and we use it both to model imprecise measurements and to identify model-free measures of irreversibility in complex many-body systems. These measures are constructed from the asymmetry of cross-correlation functions between suitably chosen observables, providing rigorous lower bounds on the entropy production. For many-particle systems, we propose a particularly practical implementation that divides real space into virtual boxes and monitors particle number densities within them, requiring only simple counting from video microscopy, without single-particle tracking, trajectory reconstruction, or prior knowledge of interactions. Owing to its generality and limited data requirements, the random coarse-graining framework offers broad applicability across diverse nonequilibrium systems.