A robust method for classification of chimera states

Chimera states are one of the most intriguing phenomena in nonlinear dynamics, characterized by the coexistence of coherent and incoherent behavior in systems of coupled identical oscillators. This paper proposes a robust method based on Fourier analysis combined with statistical classification to characterize chimera behavior. The method is applied to a system of topological signals coupled via the Dirac operator, where it successfully captures the rich dynamical regimes exhibited by the model. It provides a general and automated tool for distinguishing different dynamical regimes in complex systems, demonstrating robustness with respect to variations in network topology and system parameters.