Inferring broken detailed balance in the absence of observable currents

TL;DR

This paper presents a new method to detect time irreversibility and estimate entropy production in nonequilibrium systems using time-series data, even when observable currents are absent, applicable to complex and partially hidden systems.

Contribution

The authors introduce a novel approach that infers broken detailed balance and quantifies dissipation without needing full system information or observable fluxes.

Findings

Successfully applied to a hidden network system.

Effectively estimated entropy production in a molecular motor.

Does not require complete knowledge of system dynamics.

Abstract

Identifying dissipation is essential for understanding the physical mechanisms underlying nonequilibrium processes. {In living systems, for example, the dissipation is directly related to the hydrolysis of fuel molecules such as adenosine triphosphate (ATP)}. Nevertheless, detecting broken time-reversal symmetry, which is the hallmark of dissipative processes, remains a challenge in the absence of observable directed motion, flows, or fluxes. Furthermore, quantifying the entropy production in a complex system requires detailed information about its dynamics and internal degrees of freedom. Here we introduce a novel approach to detect time irreversibility and estimate the entropy production from time-series measurements, even in the absence of observable currents. We apply our technique to two different physical systems, namely, a partially hidden network and a molecular motor. Our method does not require complete information about the system dynamics and thus provides a new tool for studying nonequilibrium phenomena.