Quantifying hidden order out of equilibrium

TL;DR

This paper demonstrates how data compression techniques can quantify order and detect phase transitions in non-equilibrium systems, revealing new phenomena without prior knowledge of order parameters.

Contribution

It introduces a novel method using data compression to measure order and identify phase transitions in complex systems, including non-equilibrium cases.

Findings

Successfully identifies non-equilibrium phase transitions

Predicts critical exponents without prior order parameters

Reveals previously unknown ordering phenomena

Abstract

While the equilibrium properties, states, and phase transitions of interacting systems are well described by statistical mechanics, the lack of suitable state parameters has hindered the understanding of non-equilibrium phenomena in diverse settings, from glasses to driven systems to biology. The length of a losslessly compressed data file is a direct measure of its information content: The more ordered the data is, the lower its information content and the shorter the length of its encoding can be made. Here, we describe how data compression enables the quantification of order in non-equilibrium and equilibrium many-body systems, both discrete and continuous, even when the underlying form of order is unknown. We consider absorbing state models on and off-lattice, as well as a system of active Brownian particles undergoing motility-induced phase separation. The technique reliably identifies non-equilibrium phase transitions, determines their character, quantitatively predicts certain critical exponents without prior knowledge of the order parameters, and reveals previously unknown ordering phenomena. This technique should provide a quantitative measure of organization in condensed matter and other systems exhibiting collective phase transitions in and out of equilibrium.