📝 SummarXiv

Abstract

In this work, we propose a novel approach to investigate symmetry breaking in dynamical systems. In particular, we use the notion of information transfer, which provides a measure of the entropy transfer between any pair of observables in a system, to predict symmetry breaking. We show that as a system loses symmetry, its trajectories \emph{slow down} and its Shannon entropy increases. This connection enables us to use information transfer to predict the onset of symmetry breaking. Furthermore, we demonstrate the efficacy of this framework in detecting different forms of symmetry breaking using illustrative examples, including Dynamical Symmetry Breaking (DSB), Spontaneous Symmetry Breaking (SSB), and phase transitions.