Tutorial: Classifying Photonic Topology Using the Spectral Localizer and Numerical $K$-Theory
Alexander Cerjan, Terry A. Loring

TL;DR
This tutorial introduces the spectral localizer framework for classifying photonic topologies, emphasizing its application to nonlinear and radiative systems, and demonstrates its compatibility with Maxwell's equations and numerical methods.
Contribution
It provides a comprehensive, physics-oriented introduction to the spectral localizer framework, including its mathematical foundations and practical applications to various topological classes.
Findings
Framework effectively classifies local topology in nonlinear photonic systems.
Spectral localizer identifies local topological protection without a shared bulk gap.
Application to Maxwell's equations demonstrates broad applicability.
Abstract
Recently, the spectral localizer framework has emerged as an efficient approach to classifying topology in photonic systems featuring local nonlinearities and radiative environments. In nonlinear systems, this framework provides rigorous definitions for concepts like topological solitons and topological dynamics, where a system's occupation induces a local change in its topology due to the nonlinearity. For systems embedded in radiative environments that do not possess a shared bulk spectral gap, this framework enables the identification of local topology and shows that local topological protection is preserved despite the lack of a common gap. However, as the spectral localizer framework is rooted in the mathematics of -algebras, and not vector bundles, understanding and using this framework requires developing intuition for a somewhat different set of underlying concepts than…
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Taxonomy
TopicsPhotonic and Optical Devices · Neural Networks and Reservoir Computing · Optical Network Technologies
